Both of the papers assigned for today deal with computational linguistics revolving around politeness and repectfulness:

1. Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013) “A computational approach to politeness with application to social factors”.
2. Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., Hetey, R. C., Griffiths, C. M., … & Eberhardt, J. L. (2017) “Language from police body camera footage shows racial disparities in officer respect”.

I’m going to talk about the second one first because it seems to tackle a more interesting topic and asks a hard question with serious social consequences. The authors question whether black people are systematically treated differently(with less respect) by police officers? and the answer, alarmingly, is yes.

However, i don’t think the authors catered for the drastic difference in crime rate in Oakland as compared to the national average and this makes generalizability of the study a major concern.

So my question is then is that “does the oakland police department have a problem of racial profiling or do police departments in general exhibit this trend?” it’s very important to realize the difference and it’s an easy one to overlook. Another important aspect that is missing, and is pointed out by the authors as well is that the while the trends may be obvious, the reasons thereof are not! which i believe are quite important.

Finally i think without taking the language, body language and facial expressions of the people being questioned, the study might paint an incomplete picture. With modern deep-learning techniques and out of the box solutions for object detection, facial segmentation etc. such analysis is now possible.  Hence, while I feel that this study is a step in the right direction but it’s one which needs much more work.

Coming back to the first paper, before i get into the paper itself, just by reading the abstract the question the comes to mind is whether models such as the one proposed by the authors have the potential to be used for language policing and how intrusive could the effect be on freedom of speech? The reason why i raise this question, is that while we are heavily concerned about whether people face opinion-challenging information online so as to not create “echo chamber”, we tend to have the opposite stance on language used which possibly has the potential to create a “slowflake culture”. In my opinion negative experience are necessary for growth.

Nevertheless going back to the paper itself, the most interesting trends were from the stack-overflow results showing that people become less polite (haughty?) after gaining non-monetary affluence and that they becomes more polite when they loose. I guess the former goes along the lines of “humility is a hard attribute to find?”. Though i think the analysis is too one-dimensional in the sense that formality and or the necessary strictness that is required of someone when in position of power, might be perceived as less polite but no less respectful? I think a separate set of experiments needs to be run to confirm this observation and so i wouldn’t treat this particular results as a really interesting hypothesis and nothing more. Perhaps popularity of users and politeness could also be studied in a different set of experiments.

Another subtle point that the paper missed is that English is now becoming (probably has already become) a universal language and the way it’s used differs quite widely among different cultures and geographic. This seems to be a repeating trend among the papers we’ve read so far in the class. The question here becomes then what kind of effects do the local languages have on the perception of respect in (translated?) English?It may be the case that phrases when translated over from the local language to English become less-polite or maybe even rude.

Finally, i was wondering how well the study would fare with more modern NLP techniques which capture not only sentence structure but also inter sentence relationship (the proposed classifier doesn’t do that right now) and would the findings still hold or get augmented.

 

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[1] Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013). A computational approach to politeness with application to social factors. arXiv preprint arXiv:1306.6078.

[2] Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., Hetey, R. C., Griffiths, C. M.,  & Eberhardt, J. L. (2017). Language from police body camera footage shows racial disparities in officer respect. Proceedings of the National Academy of Sciences, 201702413.

The Danescu et al paper proposes computational framework for identifying and characterizing aspects of politeness marking in requests. They start with a corpus of requests annotated for politeness, specifically two large communities Wikipeidia and StackExchange. They use this to construct a politeness classifier. The classifier achieves near human-level accuracy across domains, which highlights the consistent nature of politeness strategies

The reason Danescu et al use requests are because they involve the speaker imposing on the addressee, making them ideal for exploring the social value of politeness strategies and because they stimulate negative politeness. I believe there should be a temporal aspect There is surely a qualitative difference between Wikipedia and StackExchange. The type of requests has a different nature on those two communities. This might explain the result.

Second, there is a problem I believe with respect to the generalizing this theory in the “real world”. An online community is quite different compared to a real life community, for instance a University or a corporation. In online communities people are not only geographically separated, but is truly the worst thing that can happen to someone who is not polite on Wikipedia, or Stack Exchange compared to an office environment? There would be consequences that go beyond a digital reputation. I would also be interested to conduct an experiment in those communities. What if we “artificially” established fake users with extraordinary high popularity with the same requests as users who have extremely low popularity? How polite would people respond?

Technically, there is a big difference in the number of requests on the two domains, WIKI and SE. The same size of requests from SE is ten time larger. Therefore, what puzzles me is why did they use Wikipedia as their training data instead of Stack Exchange. Why did they not use Stack Exchange

In addition, the annotators were told that the sentences were from emails of co-workers. I wonder what kind of effect that has on the results. Perhaps the annotators have specific expectations of “politeness” from co-workers that would not be the same if they knew that they were examining requests from Wikipedia and SE. Second, I see that the authors are doing a “z-score normalization” on an ordinal variable (Likert scale) which is statistically wrong. You cannot take the average of an ordinal variable. That includes the standard deviation. And nothing indicates an average of 0 in Figure 1. Instead of doing that, they can either simply report the median or use an IRT (Item Response Model) model with polytomous outcomes, which appropriate for Likert scales. In addition, while the inter-annotator agreement is not random based on the test they perform, the mean correlation is not particularly high either. Just because it is not random, does not mean that there is a consensus.

And why is the inter-annotation pairwise correlation coefficient around 0.6? The answer is different people have different notions of what they deem as “polite”. If the authors collected the demographics of the annotators, I believe we would see some interesting results. First, it might have improved the accuracy of the classifiers drastically. Demographics such as income, education, the industry that they work could have an impact. For instance, does someone who works in the Wall Street pit in Manhattan has the same notion of “politeness” as a nun?

In the second paper, henceforth Voight et al, as the title suggests, the authors investigate language from police body camera footage shows racial disparities in officer respect. They do this we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops.

I believe this paper is related a lot to the previous paper on many levels. Basically, the language displays the perceived power differential between the two (or more) agents who are interacting. Most importantly, it is the fact that there is no punishment, or there are no stakes that further bolsters such behaviors. For instance, once people lose their elections, they become politer. The power of this paper is that it is using real camera footage, not an online platform. Based on the full regression model in the Appendix, apologizing makes a big difference in the “Respect” and “Formal” models. The coefficients are both statistically significant and signs are reversed, apologizing is positive with respect, as expected.

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Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., & Potts, C. (2013). A computational approach to politeness with application to social factors. arXiv preprint arXiv:1306.6078.

Summary:

The paper focuses on exploring the content politeness on social platforms like Wikipedia, Stack Exchange. They conduct linguistic analysis exhibiting politeness on the corpus of requests and extract positive and negative politeness strategies from the context. In the experiment, two classifiers, a bag of words classifier (BOW) and a linguistically informed classifier (Ling.), are compared to illustrate the effectiveness of their theory-inspired features. They also show the relationship between politeness levels and social power/reputation. In general, we see a negative correlation between politeness and power on Stack Exchange or Wikipedia. They also add that there are differences in politeness levels across factors of interest, such as communities, geographical regions, and gender.

Reflection:

In the paper, they use a bag of words classifier as a strong baseline for the new classification task. However, it didn’t mention whether the BOW classifier is the state-of-the-art, thus the advancement of BOW needs to be discussed or some other classifiers are expected to be listed in the experiment. With a more effective classifier, where can we take advantage of it to benefit users or virtual communities? For instance, we may attach users in the community with a ‘politeness’ feature, hostile users will be automatically muted or banned if their politeness is lower than the floor value. We can also use it to evaluate the politeness of online customer service extensively used in the electronic business, which would benefit the environment of online shopping.

 

Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., Hetey, R. C., Griffiths, C. M., … & Eberhardt, J. L. (2017). Language from police body camera footage shows racial disparities in officer respect. Proceedings of the National Academy of Sciences, 201702413.

Summary:

Similar to the politeness-level difference analyzed in the above paper, one of the most non-negligible topics is the respect-level between different races(even gender, nationality). The paper collects footage from body-worn cameras, we extract the respectfulness of police officer language toward white and black community members by applying computational linguistic methods on transcripts. Disparities are shown in the speaking way of officers toward black versus white community members.

Reflection:

For the data collection, the source of datasets is not geographically diverse. Given that the issue of racial discrimination in different regions may not be of different levels of severity, I think it might be a better choice to collect footage in more than one place in the United States, not just Oakland, which would provide a holistic point of view of how officer treat white and black drivers. On the other hand, the race of officer is a key control factor since I’m curious about how black officer treats black and white drivers in their routine traffic stops, which is not discussed in detail though.
I noticed that the data they extracted were footage from body-worn cameras, transcripts were mainly used in the research. With the development of computer vision technology, extracting footage for the frame detection as an additional feature may help us to better understand the relation pattern of between police officers and drivers. For example, the frequency of conflicts between officers and drivers is also an important measure of respect degree.

Questions:

• For these topic-similar paper, is there a clear difference between being polite and being respectful?
• Since it is not easy for social scientists to measure how police officers communicate with the public, researchers use body-worn cameras to collect data. However, with body-worn cameras capturing interactions every day, how do we guarantee police officers would act exactly the same as the way when they don’t wear cameras? Based on this, keeping data collection procedure agnostic to police officers might be an alternative choice for researchers?

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Paper 1 : A computational approach to politeness with application to social factors

Paper 2 : Language from police body camera footage shows racial disparities in officer respect

Both the papers provide interesting ideas related to respect and politeness in communication. While the second paper deals with a very hot topic of unfair treatment of minority races by the police in real world, the first paper takes a more academic approach towards behavior in online space.
The authors in paper 1 develop a framework to measure politeness in online conversation which is a very valuable resource. It has words with their politness scores. They painstakingly comb through interactions of editors in Wikipedia and annotate it as polite or impolite conversation. I really liked the fact that they acknowledged that these filtering might be biased as politeness is subjective. So, they took extra measure to choose only those words which are unanimously marked by all the annotators. Once completed, they used it to apply to StackExchange conversations and found that people who were polite earlier, became less polite once they gained a certain status or power after admin elections. It is a very fascinating result. Similarly, people who lost in those elections became more polite. What could be causing this ? Is this a general with power comes arrogance argument or something else ? Did those people start getting more involved and hence, got tired of being careful with words ? Also the paper doesn’t say about the people who got popular votes but didn’t apply for admin elections. Does their behavior change too ? The paper also mentions that people from certain part of US are more polite than the others. It brings an interesting point of culture. Some people are culturally polite in conversation by US and western standards. Other people are not fluent in English vocabulary enough and could be more direct and hence, appear to be impolite. Also, sarcasm plays a big role in conversations. “Yeah, right” is a less polite phrase made up of more polite ones. The paper doesn’t talk about such limitations in its study.

The second paper is short but provides a crucial insight on how race based biases can influence a conversation. It analyses the transcripts from video feed of police officers who stopped someone and use it to determine how much polite they were in the talk. The test subjects are not revealed the race or sex of the people being investigated or the police officer but their conclusion shows that Non-Caucasians are talked less politely by the police irrespective of the race of the police officer. The paper measure in form of Respect and Formality. Though, formality is maintained for white as well black people. But it is drastically less for black people. An interesting observation is from Fig 5 that formality goes down with more time passing. Respect has a minima but increases on both ends of time of interaction. This trend can be explained by the fact that greetings are given at the start and the end of a conversation. A further direction of this paper should be facial and body gesture of the police officer as well as the apprehended user.

Overall, both papers bring attention to change in politeness either due to power and influence or racial difference which opens up new fields of study.

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This pair of papers discussed computational mechanisms and studies for determining politeness, both in online communities (Danescu-Niculescu-Mizil et al.) and in interactions with law enforcement (Voigt et al.).  In the DNM et al. paper, the authors use requests from both Wikipedia and Stack Exchange to build a politeness corpus, using Mechanical Turkers to annotate these requests on a spectrum of politeness.  They then build two classifiers to predict politeness, testing the classifiers both in-domain (training and testing data from the same source) and cross-domain (training on Wikipedia and testing on Stack Exchange, and vice versa).  The Voigt et al. paper used transcribed data from Oakland Police Department body cameras for several studies regarding racial disparity in officer behavior.  These studies included measuring respectfulness and politeness perceptions, identifying linguistic features to model respect, and measuring racial disparities in respect.

I have generalizability concerns about both papers because of the choices made in data collection.  In the DNM paper, both the bag of words classifier and the linguistically informed classifier performed worse than the human reference percentages of classification.  This was true both in-domain and cross-domain, though cross-domain is more valid for this concern.  As a result, I suspect that any new corpus that is added as a source will have classification rates similar to the cross-domain accuracy.  Further, their use cases that focus on requests provide a further bias – I suspect that introducing any new corpus not focused on requests will have similar performance, if not worse performance.  I wonder if their classifier accuracies might improve if they consider more than two sentences of text with each request, to acquire additional contextual information.

The Voigt paper used only transcribed body camera audio from a city police department, in a city well-known for crime rates.  As a result, their results may not generalize to interactions with law enforcement in rural communities (where crime rates are different), near the national borders (where demographics are different), and in safer communities (where criminal behavior is less prevalent).  Further, the behavior of the officers may differ with the knowledge that they are wearing body cameras.  I’m curious to know if patterns found in transcribed audio from police cruiser dashboard cameras (in situations when the officers aren’t wearing body cameras) are any better or worse than the results shown in this study.

In general, I also felt that the discussion sections of the papers were the most interesting parts.  The DNM paper looks at specific cases within the corpus, such as changes in the behavior of Wikipedia moderators when they become administrators and no longer have to be as polite (and who have to be particularly polite in the timeframe leading up to their election).  The Voigt paper discussion notes that while their work demonstrates that racial disparities in levels of officer respect exist, the root causes of those disparities are less clear, perhaps an ideal target for a follow-up study on a broader range of interaction transcriptions. 

 Another potential follow-up study to the Voigt paper could consider the effect of seasons on officer politeness.  All of the data from the Oakland Police Department was from interactions that occurred in April.  Are officers more likely to be polite when the weather is nicer, or less polite in the depths of winter?  And if there are seasonal or weather-related changes, does the racial disparity grow or shrink?

I found the distributions from Figure 1 of the DNM paper to be intriguing.  I’m curious why the Stack Exchange politeness spectrum seems to mimic the Gaussian distribution that you would expect to see, but the Wikipedia politeness spectrum seems to plateau just above the mean.  Trying to understand the difference in these distributions would be yet another interesting follow-up study – is the difference a result of inflated semi-polite interaction frequency because of the moderators trying to become administrators, or is it a result of the language in interactions on Wikipedia being more formal than the informal Stack Exchange, or some other reason entirely?  I’m curious to hear the thoughts of anyone else in the class.

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Summary:

The paper discusses about the influence of social networks on spreading diverse news. It analyzes the network and user connections on popular social  networking site Facebook and observe the ideologically diverse shared contents among user and their friends. Their study finds out some interesting analysis about the proportion of shares of news by diverse ideological friends of a user.

 

Reflection:

This paper studies the influence of exposure of a news shared or clicked by users of diverse contents. They find out not only users of same diverse contents share news among them, also their friends of different ideology also click the shared contents. Also, its been observed that conservative contents have highest proportion of shares. In my opinion, there are studies on users and shared contents of diverse ideological news by them, but the paper did not take age of users as a factor, which I think could be a good observation for better study the analysis.

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Paper #1: Echo Chambers Online?: Politically Motivated Selective Exposure among Internet News Users
Paper #2: Exposure to ideologically diverse news and opinion on Facebook

Summary #1:
This paper checks whether selective exposure plays any significant role in people’s choice of a news article in high-choice information environment like the Internet. The traditional approach of opinion reinforcement seeking and opinion challenge avoidance doesn’t make sense on the Internet. This paper suggests that this is due to people’s tendency to more focus on opinion reinforcement than filtering opinion challenging information. The study was run on 700+ people recruited online. Each of the participants is shown a set of articles related to a politically relevant topic and asked their interest in reading those articles in two phase. Thier reading time of the articles is also recorded. Based on the result it appears that people tend to select more stories with opinion reinforcement while spending more time on opinion challenging articles.

Summary #2:
This paper compares the influence of algorithm and user’s clickthrough in exposure to cross-cutting stories on Facebook. The network structure in Facebook is different than other social media due to connections in different offline context (school, family, social activity etc.). This is probably why homophily structure in Facebook is different. The authors use 10 million Facebook users who self-reported their political affiliation and their click activity on 7 million web URLs shared to analyze the influence. The analysis was limited to hard contents that have political significance. User’s news feed algorithm depends on various things on Facebook including how many times they visit the site daily, how much they interact with their friends, how often they click certain web URLs etc. The authors’ found that algorithmic exposure to cross-cutting content for conservative users was significantly higher than liberal users while the random probability of seeing cross-cutting content was the opposite. The click rate further limits their exposure.

Reflection #1:
This study was designed in a very simplistic way. The author has already noted that population was selected from two partisan sites which might have influenced the result. By selecting a different set of users it is easy to check if people with more partisan view are more likely to want to see the opposing view. Author’s inference of the non-existence of echo chamber is a bit of a stretch. One thing interesting in the result was that people had more interest in “gay marriage” than “civil liberty” while their reading time on “gay marriage” is less than that of “civil liberty”. This conflicts with the common notion that people spend more time things of interest. People seem to find most stories more opinion challenging than opinion reinforcing. The variance in this opinion seems to increase when people have read the stories. Another variation of this could be checking how much difference it makes in case of just headlines of a story is presented to users. The study didn’t ask if the users already read a story. This could make a significant difference given that news aggregator tend to show most popular stories first. This could also account for less time people spend on stories of interest. One thing I didn’t understand was why use log of the time to fit a linear regression model as the time span was in the range of 4sec – 9min. Another particular odd inference was author thought the correlation of reading time with Age was not significant with coefficient .01 for p < .01 while it was significant in case of opinion reinforcement (retrospective) with coefficient .02 with p < .05.

Reflection #2:
This paper checks what is the role of users click through in the limiting exposure to diverse content. Although the user selection process had a self-reporting bias, the proportion of liberal and conservative users were very close surprisingly. Figure 3 has several implications given certain condition is met. They failed to mention the number of average shares by both conservative and liberal users. Although they mentioned the total proportion of different types of shares by both conservative and liberal users, the average number of shares could skew the result. Similar relation could be found in the proportion of liberal and conservative contents in the 226,000 hard contents. They also didn’t mention users’ living area (city or rural) which could skew the percentage of ties. The authors did find a correlation between the position of a story in the news feed and click through ratio which might have affected the inference. They used a technique to calculate the risk ratio (results not shown in the supplementary text) but provided a partial method for calculation.

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Papers

[1]. Garrett, R. Kelly. “Echo chambers online?: Politically motivated selective exposure among Internet news users.” Journal of Computer-Mediated Communication2 (2009): 265-285.

[2]. Bakshy, Eytan, Solomon Messing, and Lada A. Adamic. “Exposure to ideologically diverse news and opinion on Facebook.” Science6239 (2015): 1130-1132.

Summary

In Baksy’s et al 2015 paper, the main question is: how do online networks influence exposure to perspectives that cut across ideological lines? They focus only on Facebook. To this end, they assemble data shared by U.S. users over a 6-month period between 7 July 2014 and 7 January 2015. Then, they measure ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content They construct an indicator score, “Alignment”, that ranks sources based on their ideological affiliation. They quantify the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed and further studied users’ choices to click through to ideologically discordant content. They find that compared with algorithmic ranking, individuals’ choices played a stronger role in limiting exposure to cross-cutting content.

In Garret’s 2009 paper, the author examines whether the desire for opinion reinforcement may play a more important role in shaping individuals’ exposure to online political information than an aversion to opinion challenge. In doing so, data collected via a web administered behavior-tracking study over a six-week period in 2005. The subjects were recruited from the readership of 2 partisan online news sites. The results demonstrate that opinion-reinforcing information promotes news story exposure while opinion-challenging information makes exposure only marginally less likely.

Reflection

Baksy et al published in 2015, is dealing with contemporary events that are widely discussed. The role of social media has been at the epicenter of due to its, according to some, significant impact in shaping the political landscape of the United States. Compared with algorithmic ranking, individuals’ choices played a stronger role in limiting exposure to cross-cutting content Subsequently, one question that begs an answer is whether these results remain the same now

Baksy et al specifically focused on Facebook, there is a caveat that I wonder whether the research took into account on whether people actually “Follow” their friends. For instance, what about the information users are exposed to by the things their Friends like, or share? Is this considered part of the “News Feed”? I would argue that this might have more significant effect on “Echo Chambers” than the algorithmically ranked news feed. Even in this case, you only see a subset of what your “friends” share, the friends that you actually “follow”. In addition, how about the information that you receive by the “groups” that you “follow”? I am not sure if the paper addressed this issue.

In addition, individuals may read the summaries of articles that appear in the News Feed and therefore be exposed to some of the articles’ content without clicking through.

I also find it interesting that for both liberals and conservatives the median proportion of friendships of the people on the opposite spectrum is roughly the same, around 20% following the 20/80 Pareto principle.

In Garret’s I found the experimental design particularly thoughtful an interesting, including the fact that they had screening questions. However, it should be stressed that they examine the issue of echo chambers only the in the context of politics. The dependent variable is the “use of issue related news”. It should be noted that they measured the dependent variable by their “interest in reading” and “read time”. From modeling perspective, it appears that Garret is using a “mixed model”, which is a statistical model containing both “fixed effects” and “random effects”. It might have been a good idea to include control for time trends, since there is a time wedge of six weeks in the study. Possibly including a dummy for each week would make the results more robust, although I can understand that from the author’s view 6 weeks is a short period. Still, individual fixed-effects control only for factors that remain constant between those 6 weeks.

For the logit model that examines “story selection”, the “opinion challenge” variable in the logit model has a p-value 5% to 10%. Therefore, for that model I believe the readers should place more emphasis on the results regarding “opinion-reinforcement” variable.

I wonder whether there should be some guidebook or manual that would warn and instruct us how to search information on the web-search engines. However, I don’t believe that opinion-reinforcing web searches belief is tantamount to an echo chamber. To put it bluntly, if the users are conscious that they conducting an opinion-reinforcing web-search, then what’s wrong with that? Nothing. There are degrees of “echo chambers” and whether there is critical threshold by which an echo chamber is harming the user is yet to be seen. For instance, opinion-reinforcing web search on medical issues has serious ramification on public health. Such qualitative factors impart a different contextual meaning of what an “echo-chamber” is and its ramifications. Moreover, such qualitative factors should be taken into account. The quantitative models must include more information distinctions than just “hard” or “soft” news.

Finally, I have a broader skepticism that transcends the main goal of the Baksy et al paper. Facebook, and other social media websites, are under fire for bolstering “fake news”. This criticism -financial incentive to force social media platforms to rank them higher than smaller media outlets. This is called the “principal-agent” problem in corporate finance. There should healthy skepticism.

Questions

1. The study was conducted in 2015. Would these results hold today?
2. How about effect of “Following” your friends? Does this take into account the information that you are exposed to?
3. What are the factors the about the order which users see stories in the News Feed? Do they weigh the same way?
4. There is a qualitative aspect on “echo-chambers”. The distinction of information should be more than “hard” or “soft” news. There might be an “echo-chamber” on information related to politics but not on medical/health issues. When the stakes are high, i.e. your health, are any of the hypotheses listed on Garret’s paper validated? My hunch is not. This qualitative heterogeneity is not addressed properly. I believe this requires further investigation.

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[1]. Garrett, R. Kelly. “Echo chambers online?: Politically motivated selective exposure among Internet news users.” Journal of Computer-Mediated Communication2 (2009): 265-285.

[2]. Bakshy, Eytan, Solomon Messing, and Lada A. Adamic. “Exposure to ideologically diverse news and opinion on Facebook.” Science6239 (2015): 1130-1132.

The theme of both these papers is online echo chambers that people read news or information, which favor their opinions ignoring the views of the opposing ideology. The Garrett et al. conducted a study with users recruited from two news websites, AlterNet (left-leaning) and WorldNetDaily (right-leaning). Their study was a web administered behavioral study in which participants selected articles to read in 15 minutes. The findings of the study supported the author’s hypotheses that users are more likely to view opinion-reinforcing information and will spend more time on opinion-reinforcing information.

On the other hand, Bakshy et al. performed analysis on around 10.1 million active users of Facebook who self-report their political affiliations. They examined how users interact with shared news articles by their friends as well as by algorithmically ranked news feed. Their findings were that compared with algorithmic ranking, individuals’ choices play a strong role in limiting exposure to cross-cutting content.

The Garrett et al. study was very thorough and the paper was very well written. The hypotheses were clear and the results presented backed up those hypothesis. But they assumed that everyone who visited AlterNet was left-leaning and WorldNetDaily visitors were right-leaning without any evidence to support this claim. Secondly, this sample does not ensure that it is representative of the population. Also, the length of news article should have been mentioned because the read time per story ranged from 4 seconds to 122 seconds. How can it be 4 seconds? Also, can we make a claim about read time from such study? It is highly likely that general reading patterns of people can be vastly different than the ones under study because they know that they are under experiment. It will be interesting to compare the results with people who aren’t aware that they are under study. Finally, the study is focused on three issues (gay marriage, social security reform, and civil liberties), which can have political affiliation, but will this study hold true for sports or other benign issues?

The Bakshy et al. study was interesting but had a huge limitation that it recorded engagements based on clicks. They mention that users might be reading the displayed summaries but not clicking it. This limitation makes me question the results for a reason that just because user is not clicking on opinion-challenging information does not mean that it will create an echo chamber. It is possible that user is aware of the other side of story but it is only natural that people read more of what they like and hence clicking on opinion-reinforcing information. I also felt that study on neutral people should have been more thorough and one out of context research idea that popped in my mind while reading the paper was that it will be interesting to see how ranking algorithms perform in case of neutral people? Do ranking algorithms play a role in creating echo chambers? It is highly likely that a neutral person has majority of left (or right) leaning friends and therefore, will encounter respective information and as a result ranking algorithm might confuse him with left (or right) leaning person. This seems like an interesting research direction to me.

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