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The effect of pupil size and peripheral brightness on detection and discrimination performance

It is easier to read dark text on a bright background (positive polarity) than to read bright text on a dark background (negative polarity). This positive-polarity advantage is often linked to pupil size: A bright background induces small pupils, which in turn increases visual acuity. Here we report that pupil size, when manipulated through peripheral brightness, has qualitatively different effects on discrimination of fine stimuli in central vision and detection of faint stimuli in peripheral vision. Small pupils are associated with improved discrimination performance, consistent with the positive-polarity advantage, but only for very small stimuli that are at the threshold of visual acuity. In contrast, large pupils are associated with improved detection performance. These results are likely due to two pupil-size related factors: Small pupils increase visual acuity, which improves discrimination of fine stimuli; and large pupils increase light influx, which improves detection of faint stimuli. Light scatter is likely also a contributing factor: When a display is bright, light scatter creates a diffuse veil of retinal illumination that reduces perceived image contrast, thus impairing detection performance. We further found that pupil size was larger during the detection task than during the discrimination task, even though both tasks were equally difficult and similar in visual input; this suggests that the pupil may automatically assume an optimal size for the current task. Our results may explain why pupils dilate in response to arousal: This may reflect an increased emphasis on detection of unpredictable danger, which is crucially important in many situations that are characterized by high levels of arousal. Finally, we discuss the implications of our results for the ergonomics of display design.

INTRODUCTION
You are probably reading this text as dark letters on a bright background. And if not, then you might consider doing so, because it is easier to read dark letters on a bright background (positive polarity) than it is to read bright letters on a dark background (negative polarity). This positive-polarity advantage has been well-established in human-factors research (Buchner, Mayr & Brandt, 2009;Dobres, Chahine & Reimer, 2017;Piepenbrock, Mayr & Buchner, 2014b;Piepenbrock, Mayr & Buchner, 2014a;Taptagaporn & Saito, 1990), and is often studied using proofreading experiments. For example, Piepenbrock, Mayr & Buchner (2014b) asked participants to verbally report all misspelled words in a short text. The authors found that participants read faster, and spotted more mistakes, when the text was presented in a positive polarity, as compared to a negative polarity. Findings such as these are among the reasons that most websites and word-processing software use a positive polarity.
Several researchers have suggested that the positive-polarity advantage is linked to pupil size (e.g., Piepenbrock, Mayr & Buchner, 2014b). When the background of a display is bright, the pupil constricts, compared to when the background is dark; this is the pupil light response (reviewed in (Mathôt, 2018;Mathôt & Van der Stigchel, 2015). In terms of visual perception, there are three main consequences of a bright background and the resulting pupil constriction. The first consequence is negative: A bright background, as any source of brightness, results in light scatter; that is, some of the incoming light is not focused, but instead spreads over a large part of the retina. This results in a diffuse veil of light that reduces perceived image contrast. The second consequence is also negative: Small pupils reduce the amount of light that falls on the retina, and thus reduce the signal-to-noise ratio of the image. The third consequence is positive: Small pupils suffer less from optical distortions that reduce image quality, and thus increase visual acuity (Campbell & Gregory, 1960;Liang & Williams, 1997;Lombardo & Lombardo, 2010;Woodhouse, 1975); that is, small pupils result in sharper vision. (Background luminance also affects other aspects of vision, such as contrast sensitivity, in complex ways, as reviewed by Kalloniatis & Luu (1995). Whether, and if so how, these factors also play a role in the positive-polarity advantage is not yet fully clear.) When reading text that is presented with sufficiently high contrast, as is typically the case in daily life, the advantage of increased visual acuity seems to outweigh the disadvantages of reduced signal-to-noise ratio and increased light scatter. Therefore, it is easier to read dark text on a bright background (when pupils are small), especially when the text is written in a small font (Piepenbrock, Mayr & Buchner, 2014a).
There is also some neurophysiological evidence that small pupils increase visual acuity. For example, Bombeke and colleagues (2016) manipulated pupil size by having participants covertly attend to either a bright or a dark disk, which respectively constricts or dilates the pupils, without changing eye position or visual input (cf. Binda, Pereverzeva & Murray, 2013;Mathôt et al., 2013). They then briefly presented a task-irrelevant but salient linegrating stimulus in peripheral vision, and measured the C1, an event-related potential (ERP) component that is associated with activity in primary visual cortex. The amplitude of the C1 was larger when participants covertly attended the bright disk (resulting in small pupils), compared to when they covertly attended the dark disk (resulting in large pupils). According to the authors, this result was due to the fact that small pupils improved the resolution of the C1-eliciting stimulus, in turn leading to a stronger neural response in primary visual cortex.
However, behavioral evidence for a link between pupil size and visual acuity is mixed. In a recent study by Ajasse, Benosman & Lorenceau (2018), participants made a sequence of eye movements toward a configuration of disks; each disk had a different brightness, and the size of the pupil was therefore different depending on which disk the participant was fixating. While participants were fixating a disk, two gabor patches were briefly and sequentially presented in their visual periphery. The spatial frequency of the gabor patches differed, and participants indicated which of the two had the highest spatial frequency. The authors predicted that performance on this task should increase with decreasing pupil size (and thus with increasing brightness of the fixated disk). However, they found no such relationship; that is, performance did not depend on pupil size.
The results of Ajasse and colleagues (2018) show that small pupils do not lead to improved discrimination performance in every situation. Specifically, in their experiment, stimuli were presented in peripheral vision, where acuity is mostly limited by the reduced density of cone photoreceptors; therefore, in peripheral vision, optical blur due to large pupils likely has at most a very small effect on stimulus discrimination. However, the results of Bombeke and colleagues (2016), who also used a peripherally presented stimulus, suggest that under specific conditions a small-pupil advantage can also be found in peripheral vision.
In yet other situations, small pupils may even impair visual performance (reviewed in Mathôt, 2018;Mathôt & Van der Stigchel, 2015). Specifically, detecting faint stimuli in peripheral vision requires a high signal-to-noise ratio of the stimulus relative to the background, and visual acuity is only of secondary importance. In this case, large pupils may improve the signal-to-noise ratio of vision by increasing overall light influx; more specifically, when your aim is to detect a target stimulus against a background, then large pupils will enhance the signal strength of both the target and the background, thereby reducing the influence of noise, in turn making the target more easily distinguishable from the background. Therefore, stimulus detection in the visual periphery should benefit from large pupils. When large pupils are associated with a dark environment, as is typically the case in real life, this benefit should be even stronger, because the increased signal-tonoise ratio due to large pupils is accompanied by reduced light scatter due to the dark environment. (A subtle, additional complexity comes from the fact that pupil size and light scatter are likely interdependent such that large pupils increase light scatter by increasing light influx. This may be another reason why large pupils are most advantageous in a dark environment, in which light scatter plays a relatively minor role.) However, a study by Thigpen, Bradley & Keil (2018) suggests that large pupils may not necessarily 'boost' neural responses to visual input. In their study, they presented a rapidly flickering stimulus, and measured so-called Steady-State Visually Evoked Potentials (ssVEPs): neural oscillations in visual cortex with the same frequency as the inducing stimulus. ssVEP power is believed to reflect the level of neural activity. Crucially, the authors found no relationship between ssVEP power and pupil size, and they interpreted this result as evidence for divisive normalization (Carandini & Heeger, 2012); that is, they suggested that visual responses, even in early visual cortex, are invariant to overall light influx and thus unaffected by pupil size.
The aim of the current study is to demonstrate both a small-pupil advantage for discrimination of stimuli in central vision, and a large-pupil advantage for detection of stimuli in peripheral vision. We will manipulate pupil size by manipulating the brightness of the visual periphery (which likely also affects performance independently of pupil size, as discussed above), while presenting all task-relevant stimuli on a central gray disk of constant brightness.

EXPERIMENTS 1 AND 2
The goal of Experiments 1 and 2 was to investigate whether pupil size, when manipulated through peripheral brightness, differentially affects performance on detection and discrimination tasks. In Experiment 1, participants detected, or discriminated the orientation of, a tilted Gabor patch. In Experiment 2, participants detected, or discriminated the lexicality of, a single word.

Experiment 1
Participants, ethics, and apparatus Nine naive observers participated in the experiment, after providing written informed consent. The experiment was approved by the local ethics committee of Groningen University (16163-SP-NE and 16349-S-NE). Observers were recruited from the community of Groningen University, but for privacy reasons we did not collect specific demographic information. Pupil size was recorded with an EyeLink 1,000 (SR Research). Stimuli were presented with OpenSesame 3.1 (Mathôt, Schreij & Theeuwes, 2012) on a 27'' flat screen Iiyama monitor with a resolution of 1920× 1,080 px. The monitor was not gamma calibrated. Participants were seated with their head on a chin rest about 50 cm away from the monitor.
Pupil-size manipulation Pupil size was manipulated by varying the brightness of the visual periphery (low: 0.16 cd/m 2 , medium: 8.30 cd/m 2 , high: 52.26 cd/m 2 ; see Fig. 1), which corresponded to the full display (49.22 • × 27.70 • ) except for a central gray disk. All task-relevant stimuli were presented on the central gray disk (2.84 cd/m 2 ; diameter: 25.65 • ) that was kept constant throughout the experiment.
Design The experimental task (discrimination or detection) was varied between sessions. One experimental session consisted of five blocks.
The first two blocks of each session served to calibrate a Quest adaptive procedure, which varied the properties of the target stimulus (see below) such that accuracy was kept at 75%. During these calibration blocks, peripheral brightness was set to 2.84 cd/m 2 . After these two blocks, the Quest procedure was stopped, and the target was kept constant throughout the remainder of the session. Next, participants performed three blocks of 50 trials. Peripheral brightness was varied between blocks ( Fig. 1). Block order was fully counterbalanced, such that each possible order occurred once for each participant and task. Half the participants started on the first day with a discrimination session followed by a detection session, vice versa on the second day, etc. The other half of the participants started with a detection session on the first day. In some cases, participants did more than two sessions per day. In total, participants performed 3,000 trials across 12 sessions in approximately six hours.
Discrimination task In the discrimination task (see Fig. 2A), each trial started with a central fixation dot (a uniform patch with a gaussian envelope with a standard deviation of 0.51 • [20 px] and a peak brightness of 4.41 cd/m 2 ) that was removed after 500 ms. After a random interval between 500 and 1,500 ms, drawn from a uniform distribution, a central target stimulus linearly faded in and out over a period of 650 ms. The target was a centrally presented sinusoidal grating with a gaussian envelope (a Gabor patch) with a standard deviation of 0.51 • (20 px). To maintain accuracy at 75%, the spatial frequency (range = [7.8, 11.7] cycles/ • ), contrast (range = [1, 25] %), and orientation (range = [1, 2.5] • ) of the target was varied with a Quest adaptive procedure during calibration blocks as described above.
At any point during the trial, participants pressed the left arrow key if the target was tilted counterclockwise from a vertical orientation, and the right arrow key if it was tilted clockwise (i.e., a two-alternative forced choice). The trial ended 3 s after the onset of the target. If the participant did not press any key within 3 s, a timeout was registered, and the response was considered incorrect.
Detection task In the detection task (see Fig. 2B random period drawn from a flat distribution between 1 and 2 s, a target stimulus was linearly faded in and out over a period of 650 ms. The target was identical to that of the discrimination task, except that its standard deviation was 1.02 • (40 px; i.e., twice as big), that the spatial frequency was twice as low (range = [3.9, 5.9] cycles/ • ), and that it was presented at a random point on an imaginary circle around the fixation dot with a radius of 7.70 • (300 px). At any point during the trial, participants pressed the space bar when they detected a target, and did not press any key when they did not detect a target. The trial ended 3 s after the onset of a target (when present), or after a random interval between 4 and 5 s, drawn from a uniform distribution.

Experiment 2
Experiment 2 was in most ways identical to Experiment 1, and only the differences are described below.
Participants, ethics, and apparatus Nine naive observers, most of whom had not participated in Experiment 1, participated in the experiment after providing written informed consent. All participants were native Dutch speakers.

Stimulus selection
We selected the 750 most highly frequent words between four and six characters from the Dutch Lexicon Project (Keuleers, Diependaele & Brysbaert, 2010), after manually (and based on our subjective impression) excluding overly offensive words. For each word, a matching pseudoword was generated with Wuggy (Keuleers & Brysbaert, 2010).
Design Participants performed 1,500 trials across six sessions in approximately three hours. All participants saw all words and pseudowords once in a random order.
Task Targets were (pseudo)words presented in a monospace font (Droid Sans Mono). In the discrimination task, targets were centrally presented, and participants pressed the left arrow key if the target was a pseudoword and the right arrow key if it was a word (i.e., a lexical-decision task). In the detection task, targets were peripherally presented on 50% of trials, and participants pressed the space bar if they detected a target, and did not press any key otherwise. To maintain accuracy at 75%, the font size and contrast of the target was varied.

Results
We performed the same set of analyses on both experiments. The results from both experiments were very similar.

Task performance
To be able to directly compare performance in the detection and discrimination tasks, we used accuracy (the percentage of correct responses) as our dependent measure. However, the results for the detection task are similar when using d' (a measure of sensitivity that is based on signal-detection theory). Mean accuracy on the detection task was 74.7% (Exp 1) and 73.2% (Exp 2). Mean accuracy on the discrimination task was 74.5% (Exp 1) and 75.2% (Exp 2).
To test whether pupil size (as manipulated through peripheral brightness) affects performance (see Fig. 3), and does so differently for the discrimination and detection tasks, we conducted a generalized linear mixed effects model (GLM) with response-correct as dependent variable (binomial), brightness (low [reference], medium, high), condition (detection [reference], discrimination), and the brightness × condition interaction as fixed effects. We included only by-participant random intercepts, because more complex models failed to converge. (However, the results do not crucially depend on the exact model structure.) All mixed-effects analyses were conducted with the R package lme4 (Douglas et al., 2015), in which the above model corresponds to the following formula: correct ∼brightness * condition + (1|subject_nr).
There was an effect of brightness (Exp 1: Z = −8.754, p < .001; Exp 2: Z = −8.005, p < .001), indicating that for the detection (reference) condition, accuracy decreased with increasing brightness. There was an effect of condition (Exp 1: Z = −5.343, p < .001; Exp 2: Z = −2.001, p = .045) indicating that for the low (reference) brightness, accuracy was lower for the discrimination than detection condition. Crucially, there was also a brightness × condition interaction (Exp 1: Z = 6.586, p < .001; Exp 2: Z = 4.114, p < .001), indicating that the effect of brightness was driven by the detection condition, and not present in the discrimination condition.
To confirm this, we also analyzed the discrimination condition separately, in a model with only brightness as fixed effect. Here we found no effect of brightness in Exp 1 (Z = 0.503, p = .615), and only a weak effect of brightness in Exp 2 (Z = −2.153, p = 0.031).

Pupil size
The EyeLink provides pupil size in arbitrary units. To convert these units to millimeters of diameter, we first recorded artificial pupils (black circles printed on white paper) of different sizes, and then determined a function to convert arbitrary EyeLink pupil units (au) to pupil diameter (mm): mm = −0.0324 +0. 1075× au 0.5 Mean pupil size during the detection task was 4.4 mm (Exp 1) and 5.1 mm (Exp 2). Mean pupil size on the discrimination task was 4.3 mm (Exp 1) and 5.0 mm (Exp 2).
Our brightness manipulation should have a large effect on pupil size. It is also possible that the task affects pupil size, despite the fact that the two tasks were equally difficult. To test this, we conducted a linear mixed-effects analysis (LMER) with pupil size (prior to the onset of the trial) as dependent measure and brightness, condition, and a brightness × condition interaction as fixed effects. Again, we included only by-participant random intercepts, because more complex models failed to converge (see Fig. 4).

Discussion
In summary, we found that detection performance was better with large pupils (and a dark periphery) than with small pupils (and a bright periphery). This relationship was strong, robust, and in the direction that we predicted. However, and unlike we predicted, we did not find that discrimination performance increased with decreasing pupil size (and a) Experiment 1 b) Experiment 2

Figure 4 Pupil size as a function of task and peripheral brightness in Experiments 1 (A) and 2 (B).
In both experiments, pupil size decreased with decreasing peripheral brightness. In addition, pupil size was slightly larger in the Detection (pink dots) than in the Discrimination (green dots) condition. Full-size DOI: 10.7717/peerj.8220/ fig-4 thus increasing peripheral brightness); in fact, there was a slight tendency in the opposite direction for Exp. 2. In addition, we found that pupil size was larger in the detection than in the discrimination condition, even though both tasks were equally difficult.
A limitation of our setup for measuring discrimination performance was that we could not present very small stimuli: When presented at full contrast, even the finest possible grating (i.e., 2 px/cycle) or the smallest possible letter (5× 5 pixels) could be discriminated without too much trouble by someone with normal vision. Therefore, to increase the difficulty of the discrimination task, we also reduced the contrast of the target stimulus, and our discrimination task was therefore not a pure measure of discrimination performance (a limitation that we addressed in Experiment 3).

EXPERIMENT 3
In Experiment 3, we used a setup that allowed us to present very small letters. The aim of this experiment was to investigate whether we could observe an advantage of small pupils (and increased peripheral brightness) on discrimination performance in a task that tested the limits of visual acuity. If so, this would suggest that the absence of a small-pupil benefit in Experiments 1 and 2 was due to the fact that, in these experiments, our stimuli were not sufficiently fine to test the limits of visual acuity.

Participants, ethics, and apparatus
20 naive observers participated in the experiment, after providing informed consent. The experiment was approved by the local ethics committee of Groningen University (16163-SP-NE and 16349-S-NE). Observers were recruited from the community of Groningen University, but for privacy reasons we did not collect specific demographic information. Pupil size was recorded with an EyeLink 1,000 (SR Research). Stimuli were presented with OpenSesame 3.1 (Mathôt, Schreij & Theeuwes, 2012) on three separate 7'' tablets (Samsung Galaxy Tab 7), each with a resolution of 1280× 800 px. Two tablets were presented nearby, at angle of about 50 • (relative to the direction of gaze) and a distance of about 50 cm on both sides of the participant's head, and served as light sources (see Fig. 5A). One tablet was placed in front of the participant, at a distance of 5.5 m, and served as the target display.

Task and design
Each trial started with the presentation of a black central fixation dot (R = 0.11 • [90 px]) for 500 ms on the target display. Next, a lowercase or uppercase letter ('a', 'b', 'd', 'e', 'g', 'h', 'l', 'm', 'n', 'q', 'r', or 't') was presented for 2.5 s. Letters were presented centrally in black monospace font (Droid Sans Mono). The size of the letters was varied with a Quest adaptive procedure to converge on 75% accuracy; this resulted in a range of vertical letter sizes between roughly 0.25 • and 0.08 • (visual degrees). Participants pressed the 'z' key to indicate that the letter was lowercase, and the '/' key to indicate that the letter was uppercase.
The experiment started with a practice block of 75 trials during which the background of all tablets was gray (72.15 cd/m 2 for target display; 69.01, 64.70 cd/m 2 for peripheral displays). This practice block also served to determine an appropriate font size to start with during the experimental blocks. Next, participants performed six experimental blocks during which the brightness of the light-source tablets was varied (343.30 cd/m 2 and 345.20 cd/m 2 for the two peripheral displays], medium [69.01 cd/m 2 , 64.70 cd/m 2 ], or dark [0.83 cd/m 2 , 0.88 cd/m 2 ]) while the background of the target display remained gray (see Fig. 5B). Each experimental block started with the font size that the practice block had ended with. The order of the experimental blocks followed a counterbalanced ABCABC design. In total, participants performed 375 trials in approximately 40 min.

Task performance
For each participant and brightness level separately, we took the final Quest test value as a measure of performance (font size was determined based on the Quest value, and the final Quest value corresponded to a font size resulting in 75% accuracy). To test whether peripheral brightness (and pupil size) affects performance, we conducted a linear mixed effects model (LME) with final Quest value as dependent measure and brightness as fixed effect. We included by-participant random intercepts and slopes. There was an effect of brightness (t = −2.356, p = .029), indicating that discrimination performance increased with increasing peripheral brightness (Fig. 6).
If pupil size affects performance, then we should not only find differences between the conditions in which we experimentally manipulate pupil size, but also between participants that differ in the size of their pupils. The fact that we had increased the number of participants from 9 (in Exp 1 and 2) to 20 (in Exp 3) made it feasible to look at whether individual differences in pupil size affect performance. To do so, we determined, for each participant separately, the mean pupil size during the entire experiment, and the average final Quest value (averaged across brightness levels). There was a correlation between the two measures (r = −.508, p = .022), indicating that participants with smaller overall pupils had higher performance; however, this correlation was largely driven by one participant with especially large pupils, and after excluding this participant the correlation was no longer reliable (r = −.259, p = .284).

Pupil size
To test whether our brightness manipulation affects pupil size, we conducted an LME with pupil size as dependent measure and brightness as fixed effect. We included by-participant random intercepts and slopes. There was an effect of brightness (t = −12.662, p < .001), indicating that pupil size decreased with increasing brightness of the flanking tablets. Pupil size was converted from arbitrary units to millimeters of diameter with the same procedure as used for Experiments 1 and 2.

Discussion
As predicted, we found that small pupils (and increased peripheral brightness) improved discrimination performance. In, addition we found some evidence that participants with Figure 6 Visual acuity as a function of pupil size in Experiment 3. Discrimination performance increased with increasing peripheral brightness (and decreasing pupil size). Lines correspond to individual participants (for clarity, different participants have different colors). Dots correspond to different levels of peripheral brightness, such that the highest peripheral brightness corresponds to the smallest pupil size. In addition, participants with smaller overall pupils had higher discrimination performance (although this effect was largely driven by one participant with very large pupils). The dashed line indicates the regression line.
Full-size DOI: 10.7717/peerj.8220/ fig-6 small pupils had higher discrimination performance, although this result was driven largely by a single participant with very large pupils. That is, there was a clear link between pupil size and discrimination performance, at least when pupil size was manipulated experimentally, and possibly also when considering individual differences.

GENERAL DISCUSSION
Here we report that pupil size, when manipulated through peripheral brightness, has qualitatively different effects on discrimination of fine stimuli in central vision and detection of faint stimuli in peripheral vision. Specifically, we found that small pupils (and thus a bright periphery) are associated with improved discrimination of small letters presented in central vision. This is consistent with previous studies that showed a so-called positive-polarity advantage; that is, it is easier to read dark letters on a bright background (positive polarity) than it is to read bright letters on dark background (negative polarity) (Buchner, Mayr & Brandt, 2009;Dobres, Chahine & Reimer, 2017;Piepenbrock, Mayr & Buchner, 2014b;Piepenbrock, Mayr & Buchner, 2014a;Taptagaporn & Saito, 1990). We observed this association only (but highly reliably) with very small letters that were at the limits of visual acuity. This is consistent with a previous study showing that the positive-polarity advantage is most pronounced for small letters (Piepenbrock, Mayr & Buchner, 2014a). The small-pupil benefit for discrimination is likely due to the fact that visual acuity is highest with small pupils, which suffer less from optical distortions that reduce visual acuity (Campbell & Gregory, 1960;Liang & Williams, 1997;Lombardo & Lombardo, 2010;Woodhouse, 1975;Bombeke et al., 2016).
We further found that large pupils (and thus a dark periphery) are associated with improved detection of faint stimuli that were presented at an unpredictable location in peripheral vision. This large-pupil benefit for detection is likely due to two factors. First, large pupils increase light influx, which increases the signal-to-noise ratio of vision, which in turn facilitates detection of very faint stimuli (but perhaps not, or hardly, of stimuli that are presented well above the detection threshold, as used for example by Thigpen, Bradley & Keil (2018)). Second, the dark periphery that we used to induce large pupils resulted in reduced light scatter (Lombardo & Lombardo, 2010), in turn resulting in increased image contrast, thus making it easier to detect stimuli. Therefore, reduced light scatter likely also contributed to the large-pupil benefit (which is therefore in part likely a dark-periphery benefit).
Our results offer a possible explanation for why pupils dilate in response to increased arousal (e.g., Mathôt, 2018;Mathôt & Van der Stigchel, 2015). Situations that require fine discrimination are often characterized by low levels of arousal, and situations that require detection are often characterized by high levels of arousal. For example, arousal is low when a person is reading a book, or when an animal is foraging for food. In such cases, it is crucially important to identify what you're looking at. In contrast, arousal is high when a person is afraid, or when an animal is on the lookout for predators. In such cases, it is crucially important to detect unexpected dangers. In other words, pupil dilation in response to arousal may reflect an increased emphasis on visual sensitivity, at the expense of visual acuity, to meet the demands of the situation.
An incidental yet striking result is that pupil size was larger during the detection task than during the discrimination task. Because there was no systematic difference in difficulty between the two tasks, this pupil-size difference is likely not due to differences in mental effort (which is known to affect pupil size, see e.g., Mathôt, 2018). One possibility is that, in the detection task, the pupil dilated as a result of attention being directed to peripheral rather than central vision (cf. Brocher et al., 2018;Daniels et al., 2012). An even more interesting possibility is that the pupil automatically assumes a size that is optimal for the current task, and that arousal-related pupil responses are merely one example of this general principle.
Our results also have implications for the ergonomics of display design. The idea that information is best presented as dark stimuli against a bright background (positive polarity) is well-established within human-factors research (Buchner, Mayr & Brandt, 2009;Dobres, Chahine & Reimer, 2017;Piepenbrock, Mayr & Buchner, 2014b;Piepenbrock, Mayr & Buchner, 2014a;Taptagaporn & Saito, 1990). Our results confirm this idea, but add the important caveat that this may only hold for displays that contain highly detailed information that should be discriminated; an example of such a display would be a web page that contains text in a small font. In contrast, displays that emphasize detection over discrimination may function best when bright stimuli are presented against a dark background (negative polarity); an example of such a display might be an air-traffic control display. More generally, whether a positive or a negative polarity is best depends on many factors, some of which we have highlighted in the present study. But there are many additional factors that we have not considered here, including the role of light adaptation, discomfort glare, contrast sensitivity, and the extent to which central or peripheral vision is used (Kalloniatis & Luu, 1995).
Finally, our results also speak to the recent debate about whether pupil size has been a confound in previous studies in the field of cognitive neuroscience. Bombeke and colleagues (2016) have argued that pupil size affects early visual processing; this implies that whenever an experimental manipulation affects pupil size, any difference in brain responses may be due to differences in pupil size. In contrast, Thigpen and colleagues (2018) have argued that differences in pupil size do not markedly affect early visual processing; this implies that researchers can safely interpret differences in brain responses between conditions, even if these are accompanied by differences in pupil size. Our own position is somewhere in between. It seems that differences in pupil size can measurably affect behavior and, since behavior originates from the brain, also brain activity. However, we induced pupil-size differences that were far larger than the few-percent change that is generally observed in cognitive-neuroscience experiments. And even so, we had to go through great lengths in order to find a measurable effect on visual acuity. In other words, in most previous experiments the confounding effect of pupil size has likely been small.
In summary, we have shown that small pupils, induced through a bright periphery, are associated with improved discrimination of fine stimuli in central vision. In contrast, large pupils, induced through a dark periphery, are associated with improved detection of faint stimuli in peripheral vision.

ADDITIONAL INFORMATION AND DECLARATIONS Funding
This work was supported by the Dutch organization for scientific research (NWO), Grant 016.Veni.175.078 awared to SM The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Grant Disclosures
The following grant information was disclosed by the authors: Dutch organization for scientific research (NWO): 016.Veni.175.078.


Enhancement of the QoS Parameter of LEACH Protocol using Modified K-Means Algorithm Arvinda kushwaha

880 Retrieval Number: B11640782S619/19©BEIESP DOI:10.35940/ijrte.B1164.0782S619 Abstract: Wireless Sensor Network (WSN) is a collection of battery operated sensors deployed in the monitoring area. A massive quantity of energy of the nodes is used in the internal and external communications. There is a need of energy saving mechanism for effective and efficient communication. In this paper, we propose a new Modified-LEACH (MD-LEACH) protocol for enhancing the Quality of Services (QoS) parameters. This hierarchical routing protocol (MD-LEACH) is inspired by the k-means clustering technique to consolidate sensor networks into clusters and acquire an enhanced QoS parameter. The kmeans algorithm efforts to improve the clustering procedure of LEACH protocol using Euclidean distance and prolong the lifespan of the sensor network. This algorithm forms the optimized clusters by a distance of cluster head from cluster nodes and energy of the nodes for designated the cluster heads. To evaluate the performance of the proposed approach, we used NS-2 simulator, and consider the QoS parameter, namely: packet delivery ratio, energy consumption, bandwidth, and throughput. The simulations results show that the MD-LEACH algorithm outperforms then the LEACH protocol by optimizing all QoS parameters and improved network performance.

battlefield surveillance, health care monitoring, forest fire detection, landslide detection, water quality monitoring, natural disaster prevention, structural health monitoring, data center monitoring and data logging, environmental conditions such as temperature, sound, pollution levels, humidity, wind, etc., industrial and consumer applications like machine health monitoring, industrial process monitoring and control etc., and so on. On the bases of above-discussed applications, we can categories the wireless sensor node deployment into two groups like deterministic and non-deterministic. In deterministic implementations, sensor nodes are placed in a controlled manner or manually at the selected locations where the deployment area is physically accessible such as city sense monitoring, soil monitoring, etc.
On the other hand, in non-deterministic deployments sensor nodes are deployed into physically inaccessible areas using other sources like sensors are dropped from an aircraft, e.g., battlefield surveillance and landslide detection, etc. The nondeterministic deployment is also called as random deployment. Now, we discuss the very first protocol called LEACH which we consider baseline protocol. The first hierarchical routing protocol is LEACH [1]. It enhances the QoS parameter like reduced energy consumption and optimizes delay, packet delivery ratio, bandwidth, and throughput etc. It is used the theory of data accumulation in which network is divided into a different group or clustered. Data aggregation is used for enhanced performance in terms of a lifespan of the system. There is a substantial similarity between the communication architecture of the cellular network and LEACH. The mobile network is a collection of cells where cells are called group or cluster. It is established on the signal amplitude, and a router is work as a cell header node or cluster-head. There is a specific cluster-head (CH) election method that is based on probability function, and it has many criteria that are based on the preferred percentage of Cluster heads and the many times for the duration of which a node acquired the role of CH. In this algorithm, there is a predetermined time interval which term as a round. Where every round has two phases first one is an initialization phase and the second one is a transmission phase.

Enhancement of the QoS Parameter of LEACH Protocol using Modified K -Means Algorithm
The research community is following the clustering of sensor nodes to accomplish the aim of scalability (scalability) of the network. The clustering techniques can be divided into two parts: hierarchical clustering and clustering partitioned.
The method of clustering partitioned is more well-organized and faster than the hierarchical classification and consortium is partitioned based on solid suppositions in [2]. In the perspective of clustering of manuscript mostly used partition technique is the k-means algorithm that's why we used kmeans algorithm while there are so many partitioned techniques available. In this paper, we propose a new Modified-LEACH (MD-LEACH) protocol for enhancing the Quality of Services (QoS) parameters. This hierarchical routing protocol (MD-LEACH) is inspired by the k-means clustering technique to consolidate sensor networks into clusters and acquire an enhanced QoS parameter. The kmeans algorithm efforts to improve the clustering procedure of LEACH protocol using Euclidean distance and prolong the lifespan of the sensor network. This algorithm forms the optimized clusters by a distance of cluster head from cluster nodes and energy of the nodes for designated the cluster heads. The remaining paper is systematized as follows. Section 2 presents associated work and details about k-means algorithms, Section 3 explain the proposed algorithm; Section 4 simulation results of the proposed algorithm and Section 5 present a conclusion that summarizes this work.

II. RELATED WORK
Recently many approaches are available for clustering of the sensor network to enhance the QoS of the sensor network. The classification method based on k-means has been presented which is surveyed below. In [3,4] authors presented "a protocol that is a grouping of two practices k-means algorithm and Minimum Transmission Energy (MTE). The resulted practice has distance as a condition for choosing the nodes entitled to arrangement the cluster". In [5,6] et al. presented a "balanced k-means protocol that increases the lifespan of the network over LEACH, LEACH-C, and LEACH K-Means. This methodology was founded on the space equivalent distribution and energy equivalent distribution nodes". In [7,8] authors presented a better technique of CH selection based on minimum Euclidean distance to form the clusters and to find efficient cluster head. In [9,4] authors proposed an enhanced LEACH protocol that is based on the Elbow process is used to form the cluster and find to an optimal number of "k" by using the k-means algorithm. In [11,[14][15][16] authors presented distributed Clustering techniques and in [11,[17][18][19] authors presented centralized clustering technique. In [11,[14][15][16] authors presented CH selection at the individual level or in [20,21] authors discussed a technique by using neighbor coordination. In [22][23][24][25] authors discussed different clustering techniques in wireless sensor networks. In [11] authors proposed CH selection on the random basis and in [17][18][19][20][21] authors consider some predefined parameters such as residual energy for CH selection, inter-cluster communication cost [20], distance from the sink [19], the number of neighbor nodes (node degree) [16], and timer-based approach [19] etc. While in centralized clustering, the Base station (BS) takings the full accountability of cluster creation. In this method, every node shares their all information like location, energy, and node ID to the Base station. The Base station decides the optimum clusters after evaluating the gathered data. It then broadcasts information of all the groups in the network. Upon receiving this information, each node finds its responsibility in the cluster by comparing its ID with the IDs of CH. If the node is CH, it takes the responsibility of data aggregation and data dissemination to the BS; otherwise, the node fetches its TDMA slot and goes to sleep until its transmission turn comes. LEACH-C [14] is a centralized cluster-based routing protocol. The BS runs simulated annealing optimization to determine the optimum clusters. After cluster formation, the BS broadcasts back the cluster information in the network, and then, data dissemination starts.

K-Mean algorithm:
The k-means algorithm is the most well-known partitioning method for clustering. It is the unsupervised clustering method. Grouping of date in Kmeans clustering is done on their closeness to each other by Euclidean distance. Euclidean distance is used to find out which centroid is closest to each data point, and data points are assigned to the corresponding centroids. In this k-means clustering method k denotes the number of a cluster by the closeness of data point to the centroid which is mean of the data point of a cluster is designated to that cluster which shows minimum distance. The steps of the K-Mean algorithms are as follows: Step 1: we arbitrarily choose k cluster centers (centroids).
Step 2: In this step, we calculate Euclidean distance then allocate every data point to the neighboring center.
Step3: now we calculate the average of all points assigned to the cluster, and then we obtained new centroid.
Step 4: we repeat step 2 and step3 until none of the cluster assignment change that means until our clusters remain stable, we repeat the algorithm.

III. PROPOSED ALGORITHM
In this work, we introduced an efficient cluster formation procedure using the k-means clustering algorithm called Modified-LEACH (MD-LEACH) protocol. In this algorithm, we apply K-Means partitioning method for improving the efficiency of LEACH clustering protocol. We already discussed that there are two phases in this process. First phase of communication is also called as initialization phase, in which K-means method is used to form a cluster that guarantees even groups and perfect clustering. The MD-LEACH protocol is divided into several rounds, and every round contains an initialization phase and a data transmission phase. QoS parameter is improved by using the k-means method as a clustering technique and also optimized overhead during the selection of the cluster head. Flowchart of the proposed method is shown fig 1. Initially, position of the base station (BS) is stationary, some k groups (Clusters), i, j is counter, n indicates some nodes and the sensor field are far from the BS. Transmitting and receiving energy consumption in every transmission is equal. All the sensor nodes are homogenous in the sensor field. Modified-LEACH (MD-LEACH) protocol is defined as follows: Step 1: Assign K initial centers to arbitrarily K groups.
Step 2: Calculate the distance between every data points from the centers includes them in the closest center points.
Where, = Indicate the distance between a data point and the cluster center Step 3: When all aspects have been assigned, recalculate the positions of the k centers.
Step 4: The node has been assigned the cluster head if it has the highest energy.
Step 5: Repeat Steps 4 and 5 until convergence. This yields a separation of the points into clusters from which the metric to be limited can be determined.
Step 6: Updating the centers by the following formula Step 7: Convergences are obtained when there is no longer variation in the cluster center and send information to BS.

IV. SIMULATION RESULTS
In this section, we used the simulator NS2 version 2.35 to assess the performance of the proposed MD-LEACH algorithm. We also compare MD-LEACH with LEACH protocol by considering four QoS parameter namely: PDR, bandwidth, energy consumption, and throughput. In these simulations 100 nodes arbitrarily scattered in the 100m×100m area. All nodes have 5 joules primary energy.
The base station is located at 100*150 locations. In this simulation, we used the same energy consumption model that is used in [11] and the packet size is 500kb.

A. Packet Delivery Ratio
It is the proportion of the number of packets received by the base station and some packets sent from the source [10]. It is clear that from Fig 2, MD-LEACH, PDR is higher than the LEACH protocol and PDR is decreased once maximize the density of nodes due to congestion in network, error rate, etc. The MD-LEACH generates a balanced number of cluster heads. B. Bandwidth It is the volume of data that can be transferred in a fixed amount of time. It is generally stated in bits per second (bps) or bytes per second for digital devices while it is as shown in cycles per second, or Hertz (Hz) for analog devices. From Fig.3 it gives better results once maximize the density of nodes while congestion of network, packet loss, and error rate affect the performance of bandwidth. C. Throughput Throughput is the proportion of effective data transfer over a communication network. From Fig.4, MD-LEACH performs slightly better than the LEACH protocol while throughput can be affected by the improvement of the density of node. It is transferred large number of packets to the base station because MD-LEACH generates a balanced number of cluster heads.

D. Energy Consumption
There are so many reasons for maximum energy consumption in the system. Some of them are the concentration of nodes, the distance of cluster head form cluster member, and distance of the BS from clusters in clustering. In Fig.5, energy consumption in MD-LEACH and LEACH is approximately the same. In this work, we have proposed a Modified-LEACH (MD-LEACH) protocol based k-means algorithm for WSNs. The MD-LEACH protocol is a combination of previous LEACH protocol and K-means classification. There are two phases involve designing MD-LEACH protocol. In the first phase, we select the LEACH protocoling then we applied the Kmeans algorithm on the LEACH protocol. We choose the cluster by the position of centroid and cluster head by energy as well as the location of the cluster head. The obtained results show that MD-LEACH protocol performs better than LEACH protocol for all QoS parameters. It also shows that it does reduce the overhead without packet loss in data transmission from source to destination.


Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media

Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks. We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents’ social context. We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.

Introduction
Over the last decade we witness a dramatic change in the way information is generated and disseminated. Instead of a few dedicated sources that employ reporters and fact checkers to ensure the validity of the information they provide, social platforms now provide the means for any user to distribute their content, resulting in a sharp increase in the number of information outlets and articles covering news events. As a direct result of this process, the information provided is often shaped by their underlying perspectives, interests and ideologies. For example, consider the following two snippets discussing the comments made by a Democratic Senator regarding the recent U.S. government shutdown.
thehill.com (Center) Sen. Mark Warner (D-Va.) on Sunday blasted President Trump for his "inept negotiation" to bring an end to the ongoing partial government shutdown. Warner, the ranking member of the Senate Intelligence Committee, lamented the effect the shutdown has had on hundreds of thousands of federal workers who have been furloughed or forced to work without pay.

infowars.com (Right)
Senator Mark Warner (D-Va.) is being called out on social media for his statement on the partial government shutdown. Warner blamed the "suffering" of federal workers and contractors on President Trump in a Sunday tweet framing Trump as an "inept negotiator". Twitter users pointed out that Democrats are attending a Puerto Rican retreat with over 100 lobbyists and corporate executives.
Despite the fact that both articles discuss the same event, they take very different perspectives. The first reporting directly about the comments made, while the second one focuses on negative reactions to these comments. Identifying the perspective difference and making it explicit can help strengthen trust in the newly-formed information landscape and ensure that all perspectives are represented. It can also help lay the foundation for the automatic detection of false content and rumors and help identify information echo-chambers in which only a single perspective is highlighted.
Traditionally, identifying the author's perspective is studied as a text-categorization problem (Greene and Resnik, 2009;Beigman Klebanov et al., 2010;Recasens et al., 2013;Iyyer et al., 2014;Johnson and Goldwasser, 2016), focusing on linguistic indicators of bias or issueframing phrases indicating their authors' bias. These indicators can effectively capture bias in ideologically-charged texts, such as policy documents or political debates, which do not try to hide their political leaning and use a topic-focused vocabulary. Identifying the authors' bias in news narratives can be more challenging. News articles, by their nature, cover a very large number of topics resulting in a diverse and dynamic vocabulary that is continuously updated as new events unfold. Furthermore, unlike purely political texts, news narratives attempt to maintain credibility and seem im-partial. As a result, bias is introduced in subtle ways, usually by emphasizing different aspects of the story.
Our main insight in this paper is that the social context through which the information is propagated can be leveraged to alleviate the problem, by providing both a better representation for it, and when direct supervision is not available, a distantsupervision source based on information about users who endorse the textual content and spread it. Several recent works dealing with information dissemination analysis on social networks, focused on analyzing the interactions between news sources and users in social networks (Volkova et al., 2017;Glenski et al., 2018;Ribeiro et al., 2018). However, given the dynamic, and often adversarial setting of this domain, the true source of the news article might be hidden, unknown or masked by taking a different identity. Instead of analyzing the documents' sources, our focus is to use social information, capturing how information is shared in the network, to help guide the text representation and provide additional support when making decisions over textual content.
We construct a socially-infused textual representation, by embedding in a single space the news articles and the social circles in which these articles are shared so that the political biases associated with them can be predicted. Figure 1 describes these settings. The graph connects article nodes via activity-links to users nodes (share), and these users in turn are connected via social-links (follow) to politically affiliated users (e.g., the Republican or Democratic parties twitter accounts). We define an embedding objective capturing this information, by aligning the documents representation, based on content, with the representation of users who share these documents, based on their social relations. We use a recently proposed graph embedding framework, Graph Convolutional Networks (GCN) Welling, 2016, 2017) to capture these relationships. GCNs are neural nets operating on graphs, and similar to LSTMs on sequences, they create node embeddings based on the graph neighborhood of a given node. In the context of our problem, the embedding of a document takes into account the textual content, but also the social context of users who share it, and their relationships with other users with known political affiliations. We compare this powerful approach with traditional graph embedding meth-ods that only capture local relationships between nodes.
Given the difficulty of providing direct supervision in this highly dynamic domain, we study this problem both when direct supervision over the documents is available, and when using distantsupervision, in which the document level classification depends on propagating political tendencies through social network, which is often incomplete and provides conflicting information.
To study these settings we focus on U.S. news coverage. Our corpus consists of over 10k articles, covering more than 2k different news events, about 94 different topics, taking place over a period of 8 years. We remove any information about the source of the article (both meta-data and in the text) and rely only on the text and the reactions to it on social media. To capture this information, we collected a set of 1.6k users who share the news articles on Twitter and a handful of politically-affiliated users followed by the sharing users, which provide the distant supervision. We cast the problem as a 3-class prediction problem, capturing left-leaning bias, right-leaning bias or no bias (center).
Our experimental results demonstrate the strength of our approach. We compare direct text classification or node classification methods to our embedding-based approach in both the fully supervised and distant supervised settings, showing the importance of socially infused representations.

Related Work
The problem of perspective identification is typically studied as a supervised learning task (Lin et al., 2006;Greene and Resnik, 2009), in which a classifier is trained to differentiate between two specific perspectives. For example, the bitterlemons dataset consisting of 594 documents describing the Israeli and Palestinian perspectives. More recently, in SemEval-2019, a hyperpartisian news article detection task was suggested 1 . The current reported results on their dataset are comparable to ours, when using text information alone, demonstrating that it is indeed a challenging task. Other works use linguistic indicators of bias and expressions of implicit sentiment (Greene and Resnik, 2009;Recasens et al., 2013;Choi and Wiebe, 2014;Elfardy et al., 2015). In recent years several works looked at indications of framing bias in news articles (Baumer et al., 2015;Budak et al., 2016;Card et al., 2016;Field et al., 2018;Morstatter et al., 2018). We build on these work to help shape our text representation approach.
Recent works looked at false content identification (Volkova et al., 2017;Patwari et al., 2017), including a recent challenge 2 identifying the relationship between an article's title and its body. Unlike these, we do not assume the content is false, instead we ask if it reflects a different perspective.
Using social information when learning text representations was studied in the context of graph embedding (Pan et al., 2016), extending traditional approaches that rely on graph relations alone (Perozzi et al., 2014;Tang et al., 2015;Grover and Leskovec, 2016) and information extraction and sentiment tasks (Yang et al., 2016a;West et al., 2014). In this work we focus on GCNs (Kipf and Welling, 2017; Schlichtkrull et al., 2018), a recent framework for representing relational data, that adapts the idea of convolutional networks to graphs. Distant supervision for NLP tasks typically relies on using knowledge-bases (Mintz et al., 2009), unlike our setting that uses social information. Using user activity and known user biases was explored in (Zhou et al., 2011), our settings are far more challenging as we do not have access to this information.

Dataset Description
We collected 10,385 news articles from two news aggregation websites 3 on 2,020 different events discussing 94 event types, such as elections, terrorism, etc. The websites provide news coverage from multiple perspectives, indicating the bias of each article using crowdsourced and editorial reviewed approaches 4 . We preprocessed all the documents to remove any information about the source of the article. We collected social information consisting of Twitter users who share links to the collected articles. We focused on Twitter users who follow political users and share news articles frequently (100 articles minimum). We found 1,604 such Twitter users. The list of political users was created by collecting information about active politically affiliated users. It consists of 135 Twitter users who are mainly politicians, political journalists and political organizations. The set of political users and Twitter users are disjoint. The summary of the dataset is shown in Table 1.

Data Folds
We created several data splits to evaluate our model in the supervised settings, based on three criteria: randomly separated, event separated and time separated splits. In the eventseparated case, we divide the news articles such that all articles covering the same news event will appear in a single fold. For the time-separated case, we sort the publication dates (from oldest to latest) and divide them in three folds. Each time one fold is used as training data (33%) and the other two combined as test data (66%). We use the same folds throughout the experiment of supervised classification for evaluation purpose.

Constructing the Social Information Graph
We represent the relevant relationships as an information graph, similar to the one depicted in consisting of several different types of vertices and edges, is defined as follows: • Let P ⊂ V denote the set of the political users. These are Twitter users with a clear, selfreported, political bias. They may be the accounts of politicians (e.g., Sarah Palin, Nancy Pelosi), political writers in leading newspapers (e.g., Anderson Cooper) or political organizations (e.g., GOP, House Democrats). Note that even political users that share a general political ideology can differ significantly in the type of issues and agenda they would pursue, which would be reflected in their followers.
• Let U ⊂ V denote the set of Twitter users that actively spread content by sharing news articles. The political bias of these users is not directly known, only indicated indirectly through the political users they follow on Twitter.
• Let A ⊂ V denote the set of news articles shared by the Twitter users (U ).
The graph vertices are connected via a set of edges described hierarchically, as follows: • E U P ⊂ E: All the Twitter users are connected to the political users whom they follow. Note that a Twitter user may be connected to many different political users.
• E AU ⊂ E: All the articles are connected to the Twitter users who share them. Note that an article may be shared by many different Twitter users.

Text and Graph Model
Our goal is to classify news articles into 3-classes corresponding to their bias. Since we have both the textual and social information for the news articles, we can obtain representations for them using either the text or graph models. In this section, we briefly go through the text representation methods, and then move to describe the graph based models we considered in this paper.

Text Representations and Linguistic Bias Indicators
To predict the bias of the news articles, we can consider it as a document classification task. We use the textual content of a news article to generate a feature representation. Deciding on the appropriate representation for this content is one of the key design choices. Previous works either use traditional, manually engineered representations for capturing bias (Recasens et al., 2013) or use latent representations learned using deep learning methods (Iyyer et al., 2014). We experimented with several different choices of the two alternatives, and compared them by training a classifier for bias prediction over the document directly. The results of these experiments are summarized in Table 2. Due to space constraints, we provide a brief overview of these alternatives, and point to the full description in the relevant papers.
Linear BoW Unigram features were used. The articles consist of 77,772 unique tokens. We used TFIDF vectors as unigram features obtained by using scikit-learn (Pedregosa et al., 2011).
Bias Features These are content based features drawn from a wide range of approaches described in the literature on political bias, persuasion, and misinformation, capturing structure, sentiment, topic, complexity, bias and morality in the text. We used the resources in (Horne et al., 2018b) to generate 141 features based on the news article text, which were shown to work well for the binary hyper-partisan task (Horne et al., 2018a).
Averaged Word Embedding (WE) The simplest approach for using pre-trained word embeddings. An averaged vector of all the document's words using the pre-trained GloVe word embeddings (Pennington et al., 2014) were used to represent the entire article.
Skip-Thought Embedding Unlike the Averaged word vector that does not capture context, we also used a sentence level encoder, Skip-Thought (Kiros et al., 2015), to generate text representations. We regard each document as a long sentence and map it directly to a 4800-dimension vector.

Hierarchical LSTM over tokens and sentences
We used a simplified version of the Hierarchical LSTM model (Yang et al., 2016b). In this case documents are first tokenized into sentences, then each sentence was tokenized into words. We used a word-level LSTM to construct a vector representation for each sentence, by taking the average of all the hidden states. Then, we ran another single layer unidirectional LSTM over the sentence representations to get the document representation by taking average of all the hidden states.

Graph-Based Representations
In addition to the textual information, the news articles are also part of the information network defined in Section 3. Intuitively, news articles shared by the same Twitter users are likely to have the same bias, and users who share a lot of news in common are close in their political preferences. A similar intuition connects users who follow similar politically affiliated users. Capturing this information allows us to predict the bias of a news article, given its social context. We design our embedding function to map all graph nodes into a low dimensional vector space, such that the graph relationships are preserved in the embedding space. In the shared embedding space, nodes that are connected (or close) in the graph should have higher similarity scores between their vector representations.

Directly Observed Relationships in Graph (DOR)
Our first embedding approach aims to preserve the local pairwise proximity between two vertices directly. This is similar to first-order graph embedding methods (Tang et al., 2015). There are two different relations observed in the graph: Twitter user to political user (follow) and news article to Twitter user (share). We construct our embedding over multiple views of the data, each view w corresponds to a specific type of graph relation. We can then define an loss function L w for each view w as follows: • Twitter User to Political User (UP): This objective maximizes the similarity of a Twitter user, u and all the political users in the set P u ⊂ P , where P u is the set of political users that u follows.
• News Article to Twitter User (AU): This objective maximizes the similarity of a news articles, a and all the Twitter users in the set U a ⊂ U , where U a is the set of Twitter users who shared news article a on Twitter.
All the conditional probabilities can be computed using a softmax function. Taking P (p|u) as an example: where e u and e p are embeddings of twitter user u and political user p respectively.
Computing Eq. 1 and Eq. 2 can be expensive due to the size of the network. To address this problem, we refer to the popular negative sampling approach (Mikolov et al., 2013), which reduce the time complexity to be proportional to the number of positive example pairs (i.e. number of edges in our case).
The loss defined for the two views are summed with the classification loss defined in Eq. 9 as the final loss function to be optimized in DOR embedding model.

Graph Convolutional Networks (GCN)
Graph Convolutional Networks is an efficient variant of convolutional neural networks which operate directly on graphs. It can be regarded as special cases of a simple differentiable message-passing framework (Gilmer et al., 2017): is the hidden state of node v i in the l-th layer of the neural network, with d (l) as the dimensionality of representation at layer l. N (i) is the set of direct neighbors of node v i (usually also include itself). Incoming messages from the local neighborhood are aggregated together and passed through the activation function σ(·), such as tanh(·). M (l) is typically chosen to be a (layer-specific) neural network function. Kipf and Welling (2017) used a simple linear transfor- This linear transformation has been shown to propagate information effectively on graphs. It leads to significant improvements in node classification Welling, 2017), link prediction (Kipf andWelling, 2016), and graph classification (Duvenaud et al., 2015).
One GCN layer can be expressed as follows: whereÂ is the normalized adjacency matrix, and W (l) is the layer-specific trainable weight matrix. H (l) ∈ R N ×D (l) is the matrix of hidden states in the l-th layer. H (0) = X is the input vectors. It can either be one-hot representations of nodes or features of the nodes if available. σ(·) is the activation function.
Multiple GCN layers can be stacked in order to capture high-order relations in the graph. We consider a two-layer GCN in this paper for semisupervised node classification. Our forward model takes the form: where X is the input matrix with one-hot representations and V is the representation matrix for all nodes in the graph. Figure 2 shows an example of how our GCN model aggregates information from a node's local neighborhood. The orange document is the node of interest. Blue edges link to first order neighbors and green edges link to second order neighbors.

Document Classification
The representation v of a news article (obtained with text models or graph models) captures the high level information of the document. It can be used as features for predicting the bias label with a feed-forward network.
We use the negative log likelihood of the correct labels as classification training loss: where j is the bias label of news article a.

Joint Model
Given that we have two representations available for news articles, namely the textual one and social one, it is natural to make the prediction combining both of them. We propose to align the representations of the same document from graph and text models in a joint training fashion as shown in Figure 3. The objective function for the alignment is: where e T a is the embedding for document a based on its content, and e G a is the embedding for document a based on graph structures. P (e G a |e T a ) is defined the same way as in Eq. 3. Negative sampling is again utilized to reduce time complexity.
Connecting the text and graph embedding of the same news articles, allows the bias signal to flow between the two sides. Therefore the text model may learn from the social signal and the graph model may use textual content to adjust its representation as well. We describe the loss function for the joint model in two settings -full supervision (i.e., labels associated with documents directly) and distant supervision, when bias information is only provided for a handful of users, which do not actively share documents.

Full Supervision
In the full supervision case, the loss consists of three parts, namely the classification loss of text model (L T clf ), the classification loss of graph model (L G clf ), and the loss for aligning the embeddings of the text and the graph models (L align ).
Here α, β and γ are hyper-parameters to adjust the contribution of the three parts. We set all of them to default value 1 in experiments in this paper.

Distant Supervision
Unlike the full supervision case where we have training labels for documents, we only have access to the labels of political users. However, since the text and social representation use the same space, user bias information can be propagated to the document representation, acting as a distant supervision source.
Inference Given the graph representation, decisions can be made in multiple ways. Each document has a dual representation, as a text node and a social node. Also, given the social context of a document, decision can be defined over the users that share it (assuming that users tend to share information which agrees with their biases). To take advantage of that fact, we define a simplified inference process. At test time, we can predict the bias of a news article with the embeddings from text model (Text), the embeddings from graph model (Graph), and the embeddings of sharing users who shared this article (User). The last method (User) works by averaging bias prediction scores s b u for all Twitter users that shared an article a. The bias prediction score is computed in Eq. 8 before the softmax(·) applied.
Finally, two or three of the scores listed above can be combined to make the decision.

Experiments
We designed our experiments to evaluate the contribution of social information in both the fully supervised setting, and when only distant supervision is available through the social graph. We begin by evaluating several text classification models that help contextualize the social information. Finally, we evaluate our model's ability to make predictions when very little social information is available at test time.

Implementation Details
We used the spaCy toolkit for preprocessing the documents. All models are implemented with Py-Torch (Paszke et al., 2017). Hyperbolic tangent (tanh) is used as non-linear activation function. We use feed-forward neural network with one hidden layer for the bias prediction task given textual or social representation. The sizes of LSTM hidden states for both word level and sentence level are 64. The sizes of hidden states for both GCN layers are 16. For the training of the neural network, we used the Adam optimizer (Kingma and Ba, 2014) to update the parameters. We use 5% of the training data as the validation set. We run the training for 200 epochs (50 epochs for HLSTM models), and select the best model based on performance on validation set. Other parameters in our model includes negative sample size k=5, mini-batch size b=30 (mini-batch update only used for HLSTM models). The learning rate is 0.001 for HLSTM models and 0.01 otherwise.

Experimental Results
Text Classification Results The result of supervised text classification is summarized in Table 2. We report the accuracy of bias prediction. Results clearly show that HLSTM outperforms the other methods in supervised text classification setting. Also, adding the hand engineered bias features with HLSTM representation does not help to improve performance.   Joint Model Results Table 4 shows the results of our joint model. When aligning the text and graph embeddings using joint training, both show improvement, and prediction with text or graph representations alone is better than those listed in Table 2 and 3, especially for text. Note that the increase in accuracy is much greater for the more expressive HLSTM model. Making prediction with the aggregation of multiple scores usually leads to better accuracy. Interestingly, the model's distant supervision performance is almost comparable with fully supervised text classification results. This demonstrates the strength of our joint model, and its ability to effectively propagate label information from users down to documents.
We also evaluated our model when smaller amounts of social information was available at test time. We tested our joint model with only 50% and 10% of the links for test articles kept. The results are summarized in Table 5. Clearly the performance improves as more social links are available. However, even with little social links provided in the latter case, our joint model propagates information effectively and results in an increase in performance compared to text classification.
Qualitative Analysis In Table 6, we compared the bias prediction by our text and joint model on several news articles (only titles shown in the table). These examples demonstrate the subtlety of bias expression in text, which helps motivate social representations to support the decision.

Conclusion
In this paper we follow the intuition that the political perspectives expressed in news articles will also be reflected in the way the documents spread and the identity of the users who endorse them. We suggest a GCN-based model capturing this social information, and show that it provides a distant supervision signal, resulting in a model performing comparably to supervised text classification models. We also study this approach in the supervised setting and show that it can significantly enhance a text-only classification model.
Modeling the broader context in which text is consumed is a vital step towards getting a better understanding of its perspective. We intend to study fine-grained political perspectives, capturing how different events are framed.