Author: kotut

Reflection #12 – [10/18] – [Lindah Kotut]

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  • Nicole B. Ellison, Charles Steinfield and Cliff Lampe . “The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online SNS” (2007).
  • Moira Burke, Robert Kraut and Cameron Marlow. Social Capital on Facebook: Differentiating Uses and Users (2011).

Brief:

Both papers look at bridging and bonding connections when considering social capital wrought by social media. Ellison approaches this by considering how this connections once forged offline, is then maintained online via social media — dubbing this “maintained social capital” and considering it a third dimension to bridging and bonding, while Burke examines how different uses of Facebook influence different types of social capital combining both longitudinal self-report surveys supported by (non-personal) server logs.

Reflection:
I believe that the use of Facebook as a social media test for bridge and bond ties remains relevance to this day. There is somewhat a quality control over personal posts, and it is semi-private making it easier for users to be more willing to disclose personal information. This is borne out by recent research that probed the cross-social media sites and user preferences for each and explain the shift to Instagram (they however validate the usefulness of Facebook towards building bonding and bridging ties), but for self-expression, the surveyants found Instagram to provide a medium for self-expression. Apart from Facebook as a platform (and other time-barred characteristics such as Facebook being designed around college campuses), I would also add a disclaimer that both papers are limited in longevity of their approach based on how technology has moved from the desktop to the smartphone with more opportunities for people to interact with the platform apart from the the use of Facebook has changed how often people interact and how much they check online.

Importance Social Capital: The importance of measuring social capital has only risen with time, after these two papers — so important that a study was commissioned to measure the social capital index for the United States (completed early this year). Most usefully, the study first showcased the lack of standardized measurements of social capital that leads to a discussion about what (else) to measure when talking about social capital and  provide table of sub-indices used to measure the social capital (From their results, Utah ranks first, Louisiana last and Virginia at position 17). A new measurement that shifts in tandem with the shift in Facebook use is also needed: Facebook itself is doing this, and is in the process of testing a new feature for group-to-group linking as part of advancement of measuring and using social capital.

Abusing Social Capital

For various academic and accident of nature reasons that are made clear below, I am also interested in how social capital is abused. I raise two points: From the user and the other from the platform perspective. An overarching question in using these two examples is whether they should also be included as a negative measure in connections or whether all connection leads to added social capital?

How people “cash-in” on social capital: The papers discusses at a glance how people “cash-in” on the social capital e.g. job prospects. But there are other means that are abusive: the joke about the “black friend” (extends to Muslim friend etc. as situation demands) originates from debates on racism, where a person with racist tendencies  for example, would claim the fact that they *could* not be racist because they have a black (Facebook) friend (“friend” definition stretched to gossamer thinness), the “friendship” was formed for a reason, and we can’t really fault the use of the connection, but it is abuse nonetheless. Other examples of misuse include soliciting buy-ins from friends in multi-level marketing and pyramid schemes that abuse this social capital.

Platform abusing social validation: Both Ellison and Burke’s papers were published before it became clear how Facebook intended to make money, and how this has influenced the user base (such as the rise of the #DeleteFacebook movement). Too, in planning for growth, Facebook is known to exploit user tendencies towards achieving  social validation feedback loop. that would ensure that a user spends as much time on Facebook as possible — an approach that exploits (and pollutes) the pursuit for collecting social capital.

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Reflection 11 – [10/11] – [Lindah Kotut]

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  • Cristian Danescu-Niculescu et al. “A Computational approach to politeness with application to social factors”

The authors consider politeness in discussion and their impact on power dynamics. Using crowd-labelled data from StackOverflow and Wikipedia discussions, they are able to identify politeness markers and train a classifier with those markers towards automating the labeling process. They make the data (and the resulting tool) publicly available as a part of their contribution. The politeness tool, provides further insights about how the auto-labeling works, and how the use and placement of keyword affect the general tone of the sentence.

Interesting insights beyond what makes a post/question (im)polite, they are able to distinguish politeness by region (mid-westerners are more polite), by programming language (Python programmers are the most impolite, Ruby the most polite) and gender (women are unsurprisingly more polite) — these findings serve to ground their research on a platform-independent way.

Balanced vs Power Dynamics
The major consideration of this work was in probing power dynamics, by looking at the imbalance between administrators and normal users. It would be very interesting to extend this to note the general tone of users in a balanced discourse: If there are no explicit rules in a forum on how to conduct a discussion, is there an imbalance on conversation? Does it impact the willingness of users replying if the original question is impolite? Using the same classifier to categorize these new discussions would be a straightforward step.

Bless your heart: Sarcasm and other language features
The figure below showcases aspects that are not considered by the classifier: namely sarcasm and colloquial terms. This is expected, as no classifier is perfect except with constant learning. The politeness tool also provides a means for the user to relabel the sentences according to whether they agreed on the label (and confidence) or not. Presumably there is a mechanism to improve the classifier’s prediction accuracy by having this human-in-the-loop provision. It does not impact the paper’s contribution and impact, but it continues to raise the general question on the efficacy of machine language in understanding human language.

Beyond Wikipedia and Stack Overflow
Is bot-speak polite? And are there languages markers that distinguish them from a typical user? The use of such markers would serve as a useful augment to current means of identifying bots that rely on profile features and swarm behavior. This knowledge can further be used in strengthening spam filters on places such as discussion under articles against posts with ‘bot-like’ languages.

Do users care whether their language is considered impolite? Beyond a would-be Wikipedia editor using the knowledge gained about the impact of politeness on their chances of being granted the post, does a questioner on StackOverflow or any other platform with a clear power imbalance care that their tone is impolite? Does the respondent (or the original questioner)? Or do they only care that the answer has been received? Further behaviors can then be discussed about whether the change of behavior is successful towards getting the user their desired goals, or whether the gains do not depend on the user. In which case factors such as personality — the fact that successful would-be Wikipedia administrators for example had a predilection towards the leadership position that cannot be explained by language alone (or lend them towards utilizing language skills towards getting the rewards).

Sample results from the authors’ politeness tool: http://politeness.cornell.edu/

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Reflection 10 – [10/02] – [Lindah Kotut]

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  • Kate Starbird. “Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter” (2017)
  • Mattia Samory and Tanushree Mitra. “Conspiracies Online: User discussions in a Conspiracy Community Following Dramatic Events” (2018)

Brief: The two papers extend the discussion from filter bubbles and echo chambers that are as a result of blackbox platforms and onto crippled epistemology or “intellectual isolation” that are due to the lack of source diversity due to mostly driven by user actions.

Lens: The two papers apply different lenses in looking at alternative theories surrounding dramatic events, Starbird vs Samory: Journalist/alternative media vs user lens, Twitter vs Reddit. While wanted an overall picture of the alternative media ecosystem — who the big hitters were, so followed the network approach, Samory focused on who the users are, how their behaviors both typifies them as conspiracy theorists, and how this behavior develops. Starbird’s work is also (mostly) qualitative while Samory follows quantitative analysis.

Countering Conspiracy
There is a thin line between conspiracy/alternative narrative and weaponized disinformation. The latter is touched in Starbird’s paper in discussing the #PizzaGate narrative and the role that Russian disinformation played in distributing misinformation. It is only when the line has been crossed from free speech to endangering lives that the law machinery comes to play. Before bridging that line, Samory recommends intervention before the line is breached. This is a starting point based on the quantitative analysis on the type of users in the r/conspiracy subcommunity.

This line between free speech and weaponized misiniformation allows us to apply retrospective lens from which to view both work, but especially the recommendations made in Samory’s paper. We use three examples that preceded and followed both papers:

  1. The Fall of Milo Yiannopoulos.
  2. The fall of Alex Jones.
  3. Grassroot campaigns

The three cases allow us to probe for reason: Why do platforms choose to spread misinformation? State actors aside, there other reason is for monetary reason (setting aside the argument that it is easy to churn out massive amount of content if journalistic integrity is not a concern — as notes Starbird). There is money in conflict. Conspiracy is a motherlode of conflict.

Removing the monetary incentive seems to be the way to best counter the spread/flourishing of these platforms. How to do this uniformly and effectively requires the cooperation of the platform and is subject, ironically to the rise shadow banning conspiracy (Twitter/Facebook being blamed for secretly silencing mostly conservative voices).

Why seek veteranship?
I would propose another vector of measurement to best counter theories from veterans: Why do veterans stay and continually post? There is no (overt) monetary compensation that they gain. And even if they are a front of an alternative news source, it does not square the long-game of continually contributing “quality” content to the community — which is counter to the type and volume of “news” from the alternate sources. It is easy to profile joiners and converts as they are mostly swayed by dramatic events, but not veterans. Does they also chase the sense of community with other like-minded people, or the leadership — the clout brought about by the longevity of the posters bring to bear in these decisions? Because these innate, yet almost unmeasurable goals would make it difficult to ‘dethrone’ (for lack of a better term) these veterans from being arbiters of conspiracy theories. The (lack of) turnover of the community’s moderators would also aid in answering these questions.

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Video Reflection #9 – [09/27] – [Lindah Kotut]

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Natalie Jomini Stroud. “Partisanship and the Search for Engaging News”

We can two take lessons from Stroud work in their approach:

  • Using sociological bent in studying how people make decisions, and how these decisions are reinforced, and therefore how they can be changed.
  • The impact of tone, opposing view points, engagement by journalists and the interventions by moderators (carrot + stick approach) towards the impact of discourse online.

And use them to consider a “news diet” that in conjunction with previous reading on Resnick approach to showing news bent, to propose a design featuring a nutritional label.

The design considerations are/should be  in line with the hypothesis/ concerns laid out in Strouds talk, that is:

  1. Something that does not lend to people’s predilection. If you confirm that I am a conservative, I am proud to wear that label regardless of whether that is a good thing or not.
  2. The design should not try to change a person opinion:
    a) It is dangerous and may backfire
    b) First amendment – prescribes that everyone has a right to an opinion. Civility != opinions agreed with
    c)  The entire moderation structure is subjective
  3. It should nudge towards the willingness to “listen” to the other team
  4. Nudge the opposing side to contribute in a “healthy” constructive way
  5. Points 3 and 4 are a necessary and supportive loops.

We can encapsulate these ideas in a Nutritional label – a mechanism that a user knows/understands the functions of at a glance. This fact is appreciated and has been used in previous work to classify online documents, articulate rankings  and reveal privacy considerations.

As we do not need to explain the function of the labels, we are able to concentrate on providing pertinent information to the user that can be appreciated at a glance, and that can also feature buttons (as recommended by Stroud) to nudge users towards a certain behavior.

The design is included below:

 

We can use different measurements to “nudge” the user towards civil behavior and a tendency to view more diverse news sources. An additional function would be to add thumbs up/down at the end of each bar denoting at a glance how good the user’s “diet” is.

PS: A transcription of the written notes

Ingredient Facts

  • Your diet consists of mostly right-leaning news sources, but also a number of mainstream ones. This is good, as it provides you a balanced view of the news
  • Your language: While it contains little profanity, it contains language that is considered uncivil.
    • This impacts the likelihood of your comment being featured
    • The respect other readers accord you
    • The likelihood of readers with opposing viewpoints reading your comments.

Notes

  • Data is collected from user’s comment archive e.g. Disqus/NYT
  • “Balanced diet” depends on the bend of the news source: right/left/mainstream, together with the variability of sources
  • “Respect” is a factor of “flagged comments” and “recommended”
  • Commenter’s audience: How do they lean?
  • Civility and Profanity are based on textual features
  • “Featured likelihood” can be considered a reward, something to cement user’s respect i.e. the carrot.

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Reflection #8 – [09/25] – [Lindah Kotut]

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  • R.Kelly Garrett. “Echo chambers online?: Politically motivated selective exposure among Internet news users.”
  • Paul Resnick et al. “Bursting your (filter) bubble: strategies for promoting diverse exposure.”

Should we Care?

Yes this is an important topic. Facebook is now wrestling with how best to serve balanced news to its users after the fiasco that was 2016 cycle, together with the adverse effects of echo chambers: How do you satisfy opposing views in providing information that balances for both? And how to mitigate echo chamber’s nature of amplifying misinformation and hatespeech.

Glut vs Choice
Both papers are complementary:  Garrett talks about how people consume news, and Resnick discusses how to nudge people towards consuming diverse news sources. They were written before the advent of #FakeNews, and so it does not account for the negative perception of news sources (together with the fast(er) news cycle. This glut of both news sources and news items: Social media posting links to outside news, serving on-demand video news, commentary, podcasts etc. Many more factors that fit the paper’s definition of news sources caliber would serve to give greater nuance and a new lens by which to review this work.

Skimming: then and now
I think we are past the era of news aggregation, aside from Google acting as a gate-keeper in providing a topical aggregation. There is a difference in how news is shared: Google/Twitter trending topics with teasers, the prevalence of paywalls on accessing online news paper, twitter brevity enforced by the character count etc., have led to increasing use of skimming. This runs counter to Kselly reasons this behavior to be due to prior knowledge of the news. It would be informative to see how their assumption scales to the new way news is consumed.

Variables Limitations
The dependent variable (Pop-up window) is inexact, though they account for extremes. Instead of considering active window time, scrolling behavior would’ve been a better judge of time use and provide granularity on reading behavior (i.e. skimming vs reading in depth).

On Independent variable (Perceptions on opinions): there was no delineation about political opinions formed by reading and those formed by personal belief systems that are unshaken and counters typical opinion formation. For example the author hints at gay marriage, at present, we have the issue of abortion that transcends politics and into what individuals believe in their core. It was not clear from the paper whether they considered this a lumped category. But a gradient would be useful first in separating opinions from beliefs and second, in ascertaining how best to present nudging opportunities depending on scale.

The users used in Kelly’s work were partisan. While this allowed for a good study of contrast — the authors also noting in the limitations about how this skew unnaturally compared to the general population, it also provides an inspiration on where to start/proceed with measurements: They claim that there’s an increased awareness of politics as we get older,  how much this translates to the general population would also be more useful knowledge towards using for nudging/intervention.

Other consideration/measurements

  • Whether in the process of posting/rebutting opinions the echo chamber formation also extends to the kind of friends that interact/surround an individual.
  • What is the ethics of nudging? Knowledge towards diverse source can also be used to lead other people astray.

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Reflection #7 – [09/18] – [Lindah Kotut]

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  • Thomas Erickson and Wendy A. Kellogg. “Social Translucence: An Approach to Designing Systems that Support Social Processes”
  • Judith Donath and Fernanda B. Viégas. “The Chat Circles Series: Explorations in Designing Abstract Graphical Comm. Interfaces”

Both papers contribute towards design rationale for social system design, and what role these systems play in fostering communication and collaboration. They both argue for the need for implementing social cues that take lessons from social information and applying it to the online discourse. Erickson argues with their glass door metaphor that this use leads to other, less visible transfer of mores: visibility, awareness and accountability.

Donath’s paper considered the designs such as those discussed by Erickson and articulated what users consider important, and affordances that carry over from socializing in the physical world and onto the online world (in agreement with the same concept of how to use social cues outside the physical context by Erickson).

The text-based chat system described by both papers (described by Erickson as an “abstract” communication) can be considered an egg-version of a much mature Whatsapp functionality that allows interactions between groups, a user moving between groups, the “proximity” giving access to information and the “exit” visible (giving rise to the consideration of what etiquette to follow when exiting). The audio circle described by Donath — and the visual cues it gives to observers/participants is reminiscent of Google Hangout implementation that shows the volume change via the audio bar and also serving as a pin-point on the active speaker. Tele-actor and tele-direction, also presented by Donath, while does not have a direct equivalent, is reminiscent of the Danmaku commenting system.  And while indirectly impacting a user’s action is ethically iffy, there are other, less intrusive approaches that may consider this, such as the use of Petcube app as a means to remotely control a live cat laser while donating for a cause.

“Danmaku: a video comment feature that allows comments to be overlaid on screen with no identity, apart from the traditional, Youtube-like approach of appearing below the video. Source: http://delivery.acm.org/10.1145/3150000/3148344/p209-wu.pdf

The constraints binding the discourse and in negotiating etiquette in discussion groups reveals the tensions between privacy and visibility using these themes as cues we consider the present social systems design environment

Bounds:
Who benefits from the erasure of boundaries? There is the thinnest of boundaries between social lives across the different social media. It’s a common practice for companies to create presence on all major platforms, targeting users in different ways unique to the platforms (that provide specific services). Facebook acquiring both Whatsapp and Instagram is another such boundary erasure example. Assuming the boundaries still exist, we can contrast this boundary-present phenomenon with the Weibo chat system, known to incorporate various application into one platform, we can best ponder the success/failure of the effect of boundaries on the quality of conversation.

Good faith:
I argue with Erickson’s conclusion of the need to break-down walls between people, allowing direct communication, both with groups and individuals. Facebook as a social use fits this domain: messaging and groups providing different levels of privacy, posts allowing for discourse with circle of friends etc. Erickson assumed that the platform acted as a good faith arbiter of conversation (including the use of system bots). But if the platform is the untrusted entity (meaning not trusted to keep information about the user or the contents of conversation safe), then this puts more pressure on the constraints and erodes the conversations. How this distrust bleeds into conversations is an interesting and open question.

Presenting information:
Donath’s chart circle and Erickson’s Babble: marble and circle approach, presented interesting ways to silently articulate conversation shapes visually. A new joiner to conversation is able to tell at a glance, the shape of conversation (sometime literally). How this can be used with current social system in place of/to complement notification is useful. A comparison of how users responded to the visual representation rather than notification, contrasted with how in-tune they are to a conversation (do they just scroll down and pause when the shape of conversation changes or read the entirety of previous conversations), and what are the implications? I feel like this part of research is still underutilized and can serve as useful means of providing some visual nuances to conversations.

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Reflection #6 – [09/13] – [Lindah Kotut]

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  • Sandvig et. al. “Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms”
  • Hannak et. al. “Measuring Personalization of Web Search”

The two papers can be connected by using the categorization provided by Sandvig to describe the work done by Hannak. Sandvig proposed a “systematic statistical investigation” (which is done by Hannak) in order to audit search algorithms. Two proposed categorizations fit Hannak’s work: Noninvasive user audit (with their use of Amazon Mechanical Turkers to probe for volatility in search results) and Sock Puppet Audit (using browser agents to mimic actual users and allow for scalable repeated perturbation of the search results).

1. User Preference
If I was to probe this question of auditing algorithm, I would start with a simple question/premise:
Why do people prefer Google as a search engine? This is an especially prescient question to ask since both of the papers agree on the dominance of Google as a search engine. Unlike previous work considering social media, where there is an involved investment by the user in creating and maintaining an account, there is no such burden required to use the search engine, with Bing and Yahoo among other search engines are being worthy competitors.

Understanding the users’ preferences whether overt or hidden, would give an added depth in considering then how the search engine either panders or accounts for personal… foibles in presenting results, and in Hannak’s case, what other axis of measurement is available to determine this.

It stands to reason, as Hannak points out, that given Google constantly tweaks how the algorithm works – It is a not an unreasonable deduction to conclude that part of the reason it does this is to account for hidden patterns in search habits that scale across user base.

This question can be asked retroactively as well: Has the user perception of search engine results changed if proof of filter-bubbles is presented? Or will they be very grateful for only receiving relevant search results? If a key to a successful business is about knowing your customer, then Google really knows its customers.

2. Volatility, Stability, Relevance … and censorship.
Both papers consider results returned by web search and claim that both approaches scale to other web searches. Image search included? For image search differs from web search. Case in point, the campaign by comedian John Oliver to subsume a tobacco company’s mascot with a… less glamorous version, which led to the rising of the “new” mascot to the top of image search (web search remained largely unchanged but for news articles).

Hannak’s work also note that the scope of their work is limited to US version of the search and the English language. This version can be served in another country however (by manually changing the extension back to .com). If we use the case of comparing volatility of the same search engine but different countries (one with censorship laws) Can this case be used to measure for censorship (and is censorship a form of bias)? — Because a measure for censorship can reveal which features (other than keywords) are used in the decision to censor and we can use this extrapolation to also consider other form of bias, intentional or not.

3.  Search Engine Optimization (and Bias)
The SEO, the process by which web presence can be “optimized” to appear high in search rankings with the use of tags, designed for mobile etc, so as to ensure that the page gets ranked favorably/contextually is a layman’s measure of auditing algorithms. Sandvig’s example of YouTube “reply girls” fits this description.

Thus, knowing that this deductive knowledge can be misused by those with the expertise to shape their websites to fit a particular demography — or as has been proven, successfully (and unethically), do this with targeted advertisements, raises the question of:

4. Who bears the responsibility?

Sandvig’s “Reply Girls” example was used to showcase how an algorithm can be subsumed to be an agent of discrimination. If proven to be the case, who is to be punished? If EU’s intention of assigning blame to platforms for the users who upload copyrighted content is anything to go by, then the blame will be laid on the algorithm owners in our case. But there is a trade-off to this, and it rounds back to the first point in this reflection — does the user care?

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Reflection #5 – [09/10] – [Lindah Kotut]

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  • Motahhare Elsami et. al. “‘I always assumed that I wasn’t really that close to [her]’: Reasoning about invisible algorithms in the news feed”.
  • Eytan Bakshy, Solomon Messing and Lada A. Adamic. “Exposure to ideologically diverse news and opinion on Facebook”

Reflection: 
1.Ethics
Facebook study is apt, given the recent New Yorker spotlight on Mark Zuckerberg. The piece while focusing on Zuckerberg and not the company, gives a good insight on the company ethos — that also give context to Bakshy’s claims . Considering the question of invisible algorithm: Elsami’s paper addresses it directly in outlining the benefits of not making the consequences of changes in  algorithm public, not the algorithm itself. Given the anecdotes of users who changed their mind about which users the’d like to hear more of, this is a good decision — allowing for the sense of control and trust in the algorithm curation process. Elsami’s paper proceeds to raise the concern about the effect of what the unknowns have on the decision making: When considering the (in)famous Nature paper on the large-scale experimental social vs informational messaging in affecting election turnouts and the other infamous paper on experimenting on information contagion especiall, both used millions of users’ data raise the issue of Ethics. Under GDPR for instance, Facebook is obligated to let the user know when and how their data is collected and used. How about how when the information is manipulated? This question is explicitly considered by Elsami’s paper where they found users felt angered (I thought it was betrayal more than anger from the anecdotes) after having found out design decisions that had a real-life impact — explicitly: “it may be that whenever software developer in Menlo Park adjusts a parameter, someone elsewhere wrongly starts to believe themselves to be unloved.”  

2. Irony
Bakshy’s considers their work as a neutral party in the debate about whether (over)exposure to politics is key to a healthy democracy, or whether they lead to a decreased level of participation in democratic processes. They then conclude with the power to expose oneself to differing viewpoint lies in the individual. Yet Facebook curates what a user sees in their newsfeed, and their own research showed that contentious issues promote engagement, and that engagement raises the prominence of the same content — raising the chances of a typical user viewing it. They attempt to temper this in defending the nature of the newsfeed to be dependent on the users logging/activity behavior, but this goes to show that they place the onus again on the user again … to behave in a certain manner for the algorithm to succeed and obtain consistent data?

3. Access, Scale and Subjectivity
I found it interesting about how the two papers sourced the data. Elsami et al, though they had access to respondents data, still had to deal with the throttle imposed by Facebook API. Bakshy’s on the other hand had millions of data, anonymized this disparity does not present a threat on the validity of the study, it’s just a glaring point. It would be interesting if Elsami’s work could be scaled to a larger audience — the interview process is not very scalable, but elements such as users’ knowledge on the effects of the algorithm is especially important to know how well it scales.

The issue of subjectivity manifested differently in these two works: Elsami was able to probe users on personal reasons for their actions on Facebook, giving interesting insights about decisions. Bakshy’s work regarded the use of sharing of content as a marker of ideology. What of sharing for criticism, irony, or reference?  (From what I understood, alignment was measured from the source – and click of shared link, rather than also including the commentary on the measurement). The reasons why posts are shared range from support to criticism in two extremes, and the motivation beyond the sharing makes a consequential difference in what we can conclude based on engagement. The authors note this in both the source of data (from self-reported ideological affiliation) and in their vague distinction between exposure and consumption.

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Reflection #4 – [09/06] – [Lindah Kotut]

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Reading Reflection:

  • Kumar, S., Cheng, J., LesKovek, J., and Subrahmanian, V.S. “An Army of Me: Sockpuppets in Online Discussion Communities”

Brief:
The authors make a case of the fact that anonymity encourages deception via sock-puppets, and so propose a means of identifying, characterizing and predicting sockpuppetry using user IP (if there are at least 3 posts from the same IP) and user session data (if posts occur within 15 minutes). Some of the characteristics found to be attributable to sockpuppets include the use of more first-person pronouns, fewer negations, fewer English parts-of-speech (worse-writing than average user). They also found sockpuppets to be responsible for starting fewer conversations but participate in more replies in the same discussion than can be attributed to random chance. They were also more likely to be down-voted, reported and/or deleted by moderators, and tended to have higher page rank and higher local clustering coefficient

Authors also note some concerns regarding the use of sockpuppets in discussion communities: notably, the potentiality of showing a false equivalence, as an act of vandalism and/or cheating.

Reflection:
What happens when the use deceptive sockpuppets are capable of usurping and undermining the will of the majority? I do not have a good example of a case where this is true on social media (separate from the case of battle of bots during the 2016 U.S. election cycle), but there is ample cases where this case could be examined: The FCC request for comment during the Net Neutrality debate in 2017/2018 and the saga of Boaty McBoatface serve as placeholder cautionary tales, for there was no do-over to correct for sockpuppets especially in the case of FCC. This is concern, because this phenomenon can erode the very fabric by which trust in the democratic process is built upon (beyond the fact that some of this events happened over two years ago with no recourse/remedies applied to-date). A follow-up open question would be: what then would replace the eroded system? Because if there is no satisfactory answer to this, then maybe we should have some urgency in shoring up the systems. How then do we mitigate sockpuppetry apart from using human moderators to moderate and/or flag suspected accounts? A hypothetical solution that uses the characteristics pointed out by the authors in automating the identification and/or suspension of suspected accounts is not sufficient as a measure in itself.

The authors, in giving an example of an exchange between two sock-puppets and that of a user who identifies the sockpuppet as such, reveals the presence/power of User Skepticism. How many users are truly fooled by these sockpuppets over the nuisance questions. A simplified way this can be done is a simple recruitment of users to determine whether a certain discussion(s) can be attributed to regular users or sockpuppets. This consideration can lead down the path of measuring for over-corrections:

  • is the pervasive knowledge of the presence of these sockpuppets lead to users doubting even legitimate discussions (and to what extent is this prevalent)?

This paper’s major contribution is in looking at sockpuppets in discussions/replies (therefore this point is not to detract from this contribution). On the matter of the (mis)use of pseudonyms: From a benign use-case such as the Reddit for example has a term “throw-away account” from when a regular user wants to make a discussion about controversial topic that s/he does not want to associate with their regular account, to the extreme end of a journalist using it to “hide” their activities in alt-right community discussions.

  • Can these discussions be merged, or does the fact that it does not strictly adhere to the authors’ definition disqualify it? (Because I believe that considering why users resort to the use of sockpuppets beyond faking consensus/discussion and sowing discord.)

A final point regards positive(ish) use. A shopkeeper with a new shop that wants customers can loudly hawk their wares in front of their shop to attract attention: which is to say, could we consider positive use-cases of this behavior, or do we categorize it as all bad? A forum can attract shy contributors and spark a debate by using friendly sockpuppetry to get things going. Ethical?

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Reflection #3 – [09/04] – [Lindah Kotut]

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  • Mitra, T., Wright, G.P., Gilbert, E. “A Parsimonious Language Model of Social Media Credibility Across Disparate Events“.

Brief:
Mitra et. al. approach the problem of credibility, and how to determine this from text and map language cues to perceived levels of credibility (using crowdsourcing). Based on language expressions, linguistic models (markers of modality, subjectivity, hedges, anxiety, etc) and Twitter behaviors during major (rapidly unfolding) social media events using 1% of data during an event (Unclear if both during an active event or including when the peak was considered over? “king mlk martin” collection time in Table 2 was instantaneous. Unless I misunderstood the process?). Unlike work that considers the source of in ascertaining credibility, this work looks only at the information quality in tweet (and retweets) in considering credible news. The features of the tweet: length, number of replies, retweet etc, was also included in this model as controls for the effects of content popularity.

The authors found that linguistic measures made for higher perceived credibility. Original tweet’s subjectivity (e.g. words denoting perfection, agreement and newness) serving as  a major predictive power of credibility, followed by positive emotions. On considering replies to tweets, both positive and negative emotions provided significant predictive power.

Reflection:
The authors do not claim the model be effective if deployed as-is, but would serve as a useful augment to existing/considered models. On looking at the different theorem/approaches that make up the omnibus model:

  • Emerging (Trending) events have the advantage of having a large participants contributing to it, whether in giving context etc. This work is a great follow-up of previous readings considering the problem of finding signal in the noise. Assuming an event where the majority of contributions are credible, and in English-ish. What would be the effect of colloquialism on language models? Considering “sectors” of Twitter use such as BlackTwitter where some words connote a different meaning from the traditional sense, is this effect considered in language models in general, or is this considered too fringe (for lack of a better term) to affect the significance of the whole corpus? Is this a non-trivial problem?
  • Tweet vs Thread Length: Twitter recently doubled the length of tweets to 480 characters, from 240 characters. According to the omnibus model presented by this paper, tweet length did not have a significant effect on establishing credibility. Threading — a Twitter phenomenon that allows complete thought to be written in connected tweets, allows for context giving when one tweet, or a series of disconnected tweets would not. Does threading, and the nuances it introduces, such as different replies and retweets, each tweet focusing on the different context of the whole story – have an effect on the controls effect on credibility?
  • Retrospective scoring: One of the paper’s major contributions is the non-reliance on retrospection as a scoring mechanism, given the importance of establishing credibility of news at the outset. It would be interesting to apply retrospective view on how sentiments changed given time, deleted tweets etc.
  • Breaking the model: Part of theoretical implications presented by the authors include the use of this approach towards sense making during these significant events, I wonder if the same approach can also be used to learn how to “mimic” credibility and sow discord?

P.S. Ethics aside – and in continuation of the second reflection above, is it… kosher to consider how models can be used unethically (regardless of whether this considerations are within the scope of the work or not).

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