Assigned reading: Cheng, Justin, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. “Antisocial Behavior in Online Discussion Communities.” Icwsm. 2015.

This paper talks about trolling and undesirable behavior in online discussion communities.  Authors studied data from three online communities namely CNN, Breitbart and IGN to see how people behave in these large discussion based communities, with the aim to create a topology of antisocial behavior seen in such communities, and use it for early detection of trolls. Authors have used complete time stamped data of user activity and the list of total banned users on these websites for a course of one year obtained from Disqus.

Since services like Disqus help fetch data about user profiles, it would be interesting to have users’ demographics to observe whether age, geographical orientation has anything to their said anti-social behavior. These behaviors differ greatly from one community to another i.e. gaming, personal, religion, education, politics, and news communities. The patterns also vary in gender specific platforms.

Community bias is very real. In online discussion boards or groups on Facebook, people whose opinion differs from the admin/moderator are promptly banned. One person who is fairly harmless, in fact likeable in one group can be banned in the other. It has nothing to do with their content being profane, it’s more about a difference in opinion. There are also instances where people gang up against an individual and report/harass them in the name of friendship/gaining approval from the moderator or admin of the group. Analyzing or using such data to categorize users as trolls produces inequitable results.

Some other questions which need consideration are:

 

• There is a fairly large number of people (without quoting exact stats) who have more than one accounts on social media websites. What if the banned user rejoins the community with a different identity? Can the suggested model do early detection of such users based on historical data?

 

• Does the title correctly portray the subject matter of the study? As the word anti-social refers to someone who’s uncommunicative and avoids company, but based on the results it could be seen that FBU tend to not only post more, but they also have the capability of attracting more audience and steering the topic to their liking.

 

• Having read previous articles revolving around identities and anonymity, would the results be same for communities with anonymous users?

 

• How can we control trolling and profanity in communities such as 4chan, where the actual point of unison among seasoned (but anonymous) users is trolling new users and posting explicit content?

 

• Authors also try to assess whether excessive censorship makes the users hostile or antisocial. Considering real life instances such as teacher calling out and scolding student for someone else’s mistake, would there be some users who are likely to refrain from posting altogether when once treated unfairly?

 

 

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Paper 1: Cheng, Justin, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. “Antisocial Behavior in Online Discussion Communities.” Icwsm. 2015.

Cheng et. al. in this impressive and thorough work demonstrate a way of observing and predicting antisocial behavior on online forums. They study banned users from three communities, retrospectively, to identify antisocial features of their online activity. Keeping with the Donath’s paper, we can find the similar definition of antisocial behavior here, where users act provocatively posting aberrant content.  One salient strength of this paper is well-defined problem setup and normalizing measures taken before the analysis to compensate for the peculiarities of the social media. For example, they consider strong signals such as banning and post deletion to pre-filter the bad users and also perform qualitative analysis (human evaluation) to assess the quality of posts. Their observation about bimodal post deletion distribution symbolizes the complexity of overall online interactions. It also suggests that antisocial behavior can be innate as well as adaptive. Based on the paper there are several interesting directions that can be explored.

What are the assessment signals within a group of trolls? The paper mentions several signals of antisocial behavior such as down-voting, reporting, blocking, banning etc. These signals are from the point of view of normal users and community standards. However, it will be interesting to observe whether such users identify each other and show group trolling behavior in communities. The paper mentions that the FBUs are able to garner an increased response by posting provocative content. Does this contention increase if there are multiple trolls involved on the same thread? If yes, how do they identify and support each other?

What about the users that were not banned, but are extremely antisocial on occasions? Consider a scenario in which there are two users, U1 and U2. U2 frequently trolls people of different communities by posting harmless but annoying and argument fuelling content. Based on the observations made in the paper, U2 will most likely get banned because of the community bias and post deletion. Now consider U1 who posts rarely but with extremely offensive content. U1 will most likely have their posts deleted without attracting much attention. Comparing by the number of posts deletions (which is the driving factor of this paper’s predictive model) U2 will have more likelihood of getting banned. But, which one of the two (U1 and U2) is actually more dangerous for the community? The answer is, both! To observe both types of behaviors, there should be multiple granularity levels while analyzing antisocial behavior. Per post, per thread and per user. Analyzing hateful or flaming language in individual posts can have some weight in the predictive model for deciding whether the user is antisocial or not.

Finally, will moderators be biased if they are exposed to the prediction results based on the first five posts of the user? In a scenario like this where banning and blocking infringes on the freedom of speech of the user, knowing the likelihood of a particular user being “good” or “bad” might increase community bias and the amount in which these users get censored. So, features based on more linguistic analysis are definitely relevant in which users the moderators should be warned about.

Lastly, there are few more specific questions that I want to mention:

1. Were the Lo-FBUs slower to get banned compared to the Hi-FBUs. Also, did they have a longer/shorter lifespan compared to NBUs and Hi-FBUs. This analysis might give clues about how to build more accurate predictive models for Lo-FBUs.

2. Why is Krippendorff’s alpha so low? (And how was it accepted?)
3. Can we also quantify the sensitivity of the community and use it as a feature for prediction? (Sort of customizing the banning algorithms as per the individual community’s taste. )
4. How to define antisocial behavior in anonymous communities and inherently offensive communities like 4chan?

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Summary:

This paper examines the behavior of anti social users – trolls – in comment sections of three different websites. The goal of the authors was to identify and characterize these users and separate them from the normal users i.e. the ones who are not intentionally creating problems. The paper analyzed more than a years worth of comment section activity on these sites and identified that the users who had a long history of post deletions and were banned after a while were the trolls (referred to as “Future Banned Users (FBUs)” in the paper). They analyzed the posting history and activity of the trolls, looking at post content, frequency of posting, distribution of their posting across different articles and also comparing them with non problematic users (“Never Banned Users (NBUs)”) who have similar posting activity. The trolls were found to post more on average compared to regular users, tended to have more number of posts under an article, the text in their comment replies were less similar to earlier posts compared to an NBU, and they also engaged more number of users. Trolls also aren’t a homogeneous bunch, with one section of trolls having a higher proportion of deleted posts compared to the rest and are also banned faster and spend less time on the site as a result. The results of this analysis were used to create a model to identify trolls with reasonable accuracy by examining their first 10 posts and determining whether they will be banned in the future.

 

Reflection:

This paper seems to me like an interesting follow up to the section on Donath’s “Identity and Deception” paper on trolls. Where Donath studied and documented troll behavior, Cheng et al. seems to have gone further to perform quantitative analysis on the trolls and their life in the online community. Their observation that the behavior of a troll gets worse over time due to the rest of community actively standing against them seems to be parallel to the behavior of humans in the physical world. Children that grow up abused tend to not be the most well adjusted adults, with studies showing higher rates of crime among adults who were abused or shunned by family and/or community as children compared to those who were treated well. Of course, the difference here is that trolls start off with the intention of making trouble whereas children do not. So an interesting question that we could possibly look at is: If an NBU is treated like an FBU in an online community without chance for reconciliation, will they take on the characteristics of an FBU over time?

It is interesting that the authors were able to get an AUC of 0.80 for their model I feel that is hardly sufficient (my machine learning background is minimal so I cannot comment on whether 0.80 is a relatively good result or not from an ML perspective). This is also a fact that the authors touched upon and recommended having a human moderator on standby to verify the algorithm’s claims. Considering that 1 in 5 cases are false positives, what other factors could we add to increase the accuracy? Given that these days, memes play a big role in the activities of trolls, could that also be factored in the analysis or is meme usage still too small compared to plain text to make it a useful consideration?

 

Other Questions for consideration:

1. How will the statistics change if you analyze cases where multiple trolls work together? Are they banned faster? Or can they cover and support each other in the community, leading to them being banned slower?
2. What happens when you stick a non anti social user into a community of anti social users? Will they adopt the community mindset or will they leave? How many will stay and how many will leave and what factors determine whether they stay or leave?

 

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Reading:

[1] Donath, Judith S. “Identity and deception in the virtual community.” In Communities in cyberspace, pp. 37-68. Routledge, 2002.

Summary 

This paper focused on how identity plays a key role in virtual communities. Specifically, Donath used Usenet, a structured bulletin board site that allows for multiple different newsgroups each with communal standards on how their respective communities are ran, to extrapolate how identity is established when social cues and reputations are non-existent factors. To capture how to establish identity,  Donath provides models for honesty and deception in the form of signalling taken from biology. There are tow kinds of signals mentioned: assessment signals and conventional signals where assessment signals are “directly related to the trait being advertised” giving a reliable air while conventional signals are “correlated with a trait by custom or convention” giving an unreliable air.   These signals helps provide context to the deep breakdown of a Usenet letter where writing style, personal interactions, signatures, domain address, account name/ID acted as clues, or signals, to your identity. This established a baseline for how people could point out trolls and even deceptive accounts trying to impersonate a respected member of Usenet sub-community. Donath concludes his paper by questioning how online communities can be built with improved communication with implicit social cues, but adds caution for possible unexpected social ramifications.

Reflections

The virtual environments have changed extensively since 1996 (late update to the paper). Growing up during the early stages of the internet, the idea of establishing an identity whether it be your current physical one or a new virtual identity (or even multiple virtual identities) is an intricate problem. While Usenet provided a great example of extrapolating key features to establish one’s identity, Usenet is now a very outdated system. New social platforms like Facebook, Twitter, Reddit, Instagram, etc. have come into the light. What are some new cues for trusting one’s identity on the internet? For example, with data breaches being the norm in today’s society where does identity theft come into play here? Can establishing a identity virtually help protect your physical one? Additionally, with so many social media platforms, trying to keep one identity across all platforms would be a hard problem.  I would even challenge that we now can see social cues given a particular social media platform unlike Usenet.

Questions

• What are new ways of establishing identity and noting deception? Would it still be through assessment and conventional signaling?
• What new designs methodologies could be utilized to strengthen one’s identity online and in the physical world?
• Does identity theft encourage establishing yourself online? Could this be used to help retain you identity?

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Reading:

[2] Bernstein, Michael S., Andrés Monroy-Hernández, Drew Harry, Paul André, Katrina Panovich, and Gregory G. Vargas. “4chan and/b: An Analysis of Anonymity and Ephemerality in a Large Online Community.” In ICWSM, pp. 50-57. 2011.

Summary 

This paper explored 4chan’s /b/ forum community with goals to quantify ephemerality of /b/ and an analysis of identity and anonymity. 4chan is a forum board known for generating (among other things) memes and being the backbone to some of the internet’s culture. The author’s are pursuing  an anomaly where 4chan’s /b/ forum functions entirely of anonymous users and is extremely ephemeral which is not entirely supported through related works. This presented an opportunity to explore a large online community in the wild where design implications can be extrapolated for online social environments. The first study engaged in data collection over a two week time period where the lifetime of all threads was observed. With the forum moving at such a fast pace it correlated with high ephemerality where actions like bumping and sage, forms of extending a thread’s lifetime, were also observed. The second study looked at the impact of anonymity which left a user’s identity hidden. The same two week sample was used where a 90% of all user’s remain anonymous while posting. But the interesting outcome was anonymity is viewed as a positive feature where open conversation can thrive which supports experimentation with new ideas (such as memes). Interestingly enough, one’s identity is never known but how a user interacts in the community gives an air of seniority to the community (not including moderators).  The author’s conclude the paper seeking to conduct a closer study on the content of 4chan and its users.

Reflections

For the first study, I’v never really considered the pace of the threads before. In fact, how the threads work seems unique in that what is interesting to the current crowd at that moment. Of course, with Figure 3 from the paper does a great job highlighting this.

The authors show culture of /b/ is highly influenced with this fast paced environment (enough that users of /b/ cope with capturing some threads by download images as they come in). It begs the question of what design decisions could we grasp from a high paced, highly influential community and culture in another social platform? 

As someone who is quite similar with 4chan, not as a regular user but more as a sparse lurker over the years of its existence, I found study 2’s results unsurprising. Through observation over the years it was unsurprising to see the high usage of the anonymous (anon) for the majority of the posts. Although, how can we use 4chan’s successful anonyomous structure to help support today’s online identity? Donath mentioned looking at key elements of a Usenet message, but we lose account name/ID and email address information. Would identifying writing style be enough? Would this open up identity deception? The author’s do offer some comments here: “Instead, the /b/ community uses non-technical mechanisms like slang and timestamping to signal status and identity.” This does align with Donath’s comments to some degree, but it would be interesting to see if is enough for identity if a platform is to support mass anonymous users.

Questions

• What other platforms can take advantage of anonymous interaction between users? Can this feature be extrapolated to a specific problem domain?
• Does content/memes change anonymous interaction long term that it’s hard to follow those who anonymous bat have been around for a long period of time?

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The main topic of discussion for todays’ reflection was Identity, deception and anonymity. The papers assigned for this assignment are

1. Donath, Judith S. (1999)- “Identity and Deception in the Virtual Community. Book chapter from “Communities in Cyberspace” (29-59).
2. Bernstein, Michael S. et al. (2011) – “4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community”. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (50-57).

Summary.

The first paper by Judith S. Donath talk about the identity and the deception that is prevalent in the online community. For example, a person can claim to be an expert of a matter or can falsely embody someone else so on and so forth. The paper mainly focuses on the Usenet newsgroup which is predominantly a non-fiction based virtual community. It mainly discusses how reliability of a post is closely based on the writers’ credibility. It talks about how the information from a post e.g., the writers’ email address, the language and tone of the post itself, the signature, can be used to detect various attributes about the author of the post. Attributes like the location, organization, gender etc. are some which may be detected. Judith also describes how trolls are common in those chat forums and that often it has even led to contacting the system administrators of these offenders to act against them.

The second paper focuses mainly on the anonymity and the ephemerality of posts in the large online community of 4chan, and its most popular board named /b/. The authors conduct two kinds of study to test the ephemerality of the posts itself and the identity and anonymity of user to understand its effects. It gives an idea about what kind of content the community wants, which results in the post to have a relatively longer life and even been re-posted later on. The concept of “bumping” and “sage” is described which gives the user control over the ephemerality of the posts. It was found that over 90 percent of the posts on /b/ was completely anonymous. Email signatures were also uncommon with 98.3% of posts not containing an email. It was also found that only 0.05% of posts had tripcodes and pseudo names.

Reflection.

The first paper gives an idea about how identity plays an important role in the virtual community. It also points out ways by which one can somewhat get an essence of the post is trustworthy or just a “troll”. One thing that I could relate to right away is how I tend to rely on articles and posts in social forums like Quora, Twitter, mac-forums or Stack Overflow is quite similar. I have noticed that I tend to look at the persons’ name, the email and his description. The blue tick of twitter, or the name and description of Quora, the tag attached to the author in mac-forums (Administrator, Moderator, Member, Premium-Member etc.) and the number of up votes (or the green tick) in a stack overflow post makes me decide if I want to believe or follow the particular article. It is true that sometimes it turns out to be dubious and I am completely directed in the wrong route which leads me to believe that trusting such users, based on only the signatures and other attributes is erroneous.

The second paper mainly highlights how common anonymous posting is in the realm of the virtual world. There is a need for anonymous posting where people seeking help or advice without giving up the identity can benefit from this. This helps in keeping certain sensitive matters private. Nonetheless, on the other hand, in absence of any form of authentication, a suspicious individual can exploit the system due to the lack of accountability.  It was pointed out by the author that /b/ is “crude” with contents often being “intentionally offensive”. because of its anonymous nature. The main point that stands out to me here is, when it comes to valuable information, then user identification is of greater importance and the worth of the information is related to the credibility of the one posting it.

The question of ephemerality is the one which concerns me the most. Although /b/ is ephemeral by design (where a post is automatically deleted once it reaches the fifteenth page) but, our today’s social media platforms are not ephemeral in general. Facebook keeps on remaining me what I did 4 years ago or my n^th friendship anniversary with someone. This suggests that all our details and data are stored, analyzed and later utilized to give us a personalized feed. The “My Activity” of google which records everything, even things being asked to a google home speaker. The concept of “digital footprint” thus arises.

Read More

1)- Donath, Judith S. “Identity and deception in the virtual community.” Communities in cyberspace, 2002

2)- Bernstein, Michael S., et al. “4chan and/b: An Analysis of Anonymity and Ephemerality in a Large Online Community.” ICWSM, 2011

 

Summary:

After almost a decade of the rapid evolution of online social communities, the two papers discussed a continually important component when designing an online social platform which is Identity and Anonymity of users.

The first paper – although dated – discussed a phenomenon still arises in modern online social platforms which is user Identity and how it could be used to deceive others. The author used an analytical framework to approach her study, which is a communication system developed by biologists and game theorists that consists of signals (cues used to present identities), senders (person having/claiming an identity) and receivers (other members of the community).

The author classified the signals into: (1) Conventional (obvious, easy and cheaper) signals, which might deceive the community members, and (2) Assessment signals, which are harder and more costly to send, and usually used to measure the veracity of an identity

The author studied the Usenet newsgroups, and identified three major types of identities: (1) Account name (email address), (2) Identity in voice and Language and (3) Signature. She also identified different types of deception inside the Usenet newsgroup: (1) Trolls, (2) Category deception, (3) Impersonation and (4) Identity concealment. And through each type of identity and deception the author explained different examples and showed which type of signals is used and how the other community members had recognized the deception.

In the second paper the authors studied the 4chan online community with respect to the Anonymity nature of the participants and the design aspect of ephemerality of the network.

Reflections:

Lots of observations and events related to the Egyptian revolution in 2011 came into my mind when I read the first paper. During those events online social platform were used to discuss the ongoing political situation between different parties, organize protests, and even rescue protesters during that period. The following examples illustrate real situations about identity and deception during the revolution:

• Protests leaders used to conceal their real identities through creating false identities in order to protect themselves from the authorities, which used to chase and detain protesters (Identity concealment)
• Fake accounts were used to create conflicts and fights between different political parties and even between the members of a single group/party (Trolls)
• Fake identities that worn the hat of certain groups were used to hack into those groups in order to create conflicts between the same group members (Category deception)
• Fake accounts of popular participants of the revolution were used to spread false news to their followers (Impersonation)

Those cases of different types of deception create an ongoing argument whether the identity of users on online social platforms should be kept verified and reflects the real identity of the user or users could be anonymous or use fake identities.

Designing an online social platform that allows its users to freely self-express, participate in events organized through those platforms and protects their privacy while in the same time prevents trolling, identity impersonation and different types of deceptions is a challenging task.

The following question came into my mind when I read about the in-voice and language identity. Could we identify/classify users’ views/values/backgrounds from their languages and writing styles? I believe this a very challenging and interesting research question and there are some ongoing work to answer it.

Another question about identity, suppose a user who has a real identity on one of the online social platforms created several fake identities on the same platform. How distant those fake identities far from the real identity? And are there any patterns/commons between those fake identities? Could it be used to psychoanalyze the person behind them?

The second paper signifies the role of Anonymity in the growth of the 4chan community, while the first paper lists many cases to prove the importance of identity veracity in Usenet and how identity could be used to deceive others. I think the decision of Anonymity vs Identity depends on the nature of the social platform. For example, Usenet is used as question answering platform where users search for credible answers to their questions, that is why Anonymity is not suitable and may be dangerous in designing such network. On the other hand, the nature of 4chan does not require the identity of the contributors to be visible or checked. That is also why Anonymity contributed to the energy growth of 4chan.

Another question arises, does the ephemeral nature of the platform helps in creating new trends and discussions or do people keep interacting/discussing in the same topics/trends even if those trends and topics are no longer exit on the platform?

 

Read More

1. Donath, Judith S. “Identity and deception in the virtual community.” Communities in cyberspace. Routledge, 2002. 37-68.
2. Bernstein, Michael S., et al. “4chan and/b: An Analysis of Anonymity and Ephemerality in a Large Online Community.” ICWSM. 2011.

The value associated with human speech stems not only from its contents but various other factors namely the identity of the speaker, the speaker’s place in the social hierarchy, speaker’s speech characteristics and so forth. But speech in the virtual world is devoid of these physical entities due to which other markers tend to occupy its place as substitutes. What are the challenges in such environments? This is what the authors of the two papers are trying to probe and understand but from different angles.

[1] tries to map an evolving virtual environment, its use of identity and virtual equivalent of significance. It also describes various cues evolved in these settings to bolster the validity and importance of these identities created. The author goes on to then describe the various harms in giving importance to identity cues and eventually these virtual identities as they are susceptible to deception and impersonation. The author also quickly goes on to describing the effects of persistence of content on the behavior of the virtual crowd.

Although the virtual space described, usenet, is a thing of the past, few of the problems that plagued it are still prevalent in today’s virtual spaces. An interesting point the paper dealt upon, that I find interesting is the idea of harm due to taking advice from virtual and potentially unreliable sources. This brings up a rather interesting question, who is responsible for the physical harm due to signal or information from these virtual spaces? How can such incidents be managed? This cyber-physical interaction is relevant especially now, given that social media is a platform for much of the political and social debate.

[2] looks at 4chan, a imageboard website to understand the effect of anonymity and ephemerality in a virtual space where both those qualities are “deemed to be undesirable.” The author, conducts two experiments, one to identify the ephemerality of the content which in the virtual space is controlled through various mechanisms, and the other to identify the different identity cues which have gained prominence due to absence of traditional ones.

Given, the above feature of anonymity and ephemerality, 4chan is an interesting space to explore. Various other products like Instagram or Snapchat stories have the feature of ephemerality but not the anonymity like 4chan. But these ephemeral entities, I believe, have a common trait which makes it pool in more engagement from the users due to its inherent design, giving it an edge over non-ephemeral medias in certain cases. But this begs a question, is the user’s mental health due to it playing a part in increasing active user login time is something these product makers care about and if so what are the different steps that can be taken to manage it? I also find the natural conception and evolution of alternate forms of identity using to enforce group membership and status barrier interesting intriguing. One can also see that users having to use a product can create mechanisms rather naturally which the designer himself didn’t think about and allowing for such use helps in creating a more successful product.

Looking at various virtual spaces which have different design principles including those of how they manage identity, how one manages conflicts, if one’s virtual spaces have moderators and area specific rules, one can observe that the users and their behaviour tend to different. This is an interesting phenomenon. These identities in usenet as seen in [1] are more individualistic along with few group identity signifiers compared to 4chan as seen in [2]. This shows how the design of the virtual spaces creates very different user experience. How we manage these will definitely shape the conversations in the coming years.

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Papers:

1. Identity and deception in the virtual community – Judith S. Donath.
2. 4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community – Michael S. Bernstein, Andres Monroy-Hernandez, Drew Harry, Paul Andre, Katrina Panovich and Greg Vargas.

Short Summary:

It goes without saying that both the papers deal with issues related to identity of the users on an online social platform. Both spend some time describing both how the design of these systems affects trade-offs between the credibility, status, reputation building, and accountability that identity affords the user on the one hand, and the freedom from consequence, judgement, and equality of consideration that anonymity provides on the other hand. Both papers also discuss the ways in which users deal with or adapt to the design of these systems, creating their own methods to tip the balance of these trade-offs, as befitting the situation.

[1] focuses on the users of Usenet, an online topic-based chat/advice forum. It broadly discusses identity, attempts at deception of identity, and mechanisms for identity verification and the prevention of deception that users create on Usenet.

[2] discusses not just identity, but particularly how anonymity and the transitory nature of data on the website affects content, and user interaction and behavior. It focuses on the random (/b/) thread of the website, 4chan.

Reflections:

[1] describes the concept of signalling as a way to establish possession of a desirable trait, e.g. experience in a community, or domain knowledge of the topic of the community discussion. In particular, [1] mentions examples of signatures which contain programming jokes, “Geek Code“, or riddles used in programming Usenet communities. [2] also mentions similar “signatures” in 4chan /b/, like the “triforce“. It might be interesting to explore the usage of such explicit “signatures” on modern anonymous or pseudonym based platforms, like Reddit, StackOverflow, etc.

More ubiquitous are implicit signalling mechanisms, like the language, grammar, references, usernames/email-ids, etc. While investigating this in a data-driven way might be harder, it would also be interesting to collect data about the trends in usage of in-group language and references of a single new user over time, as the user goes from being a new member to a seasoned member of the group , and starts using the same language and references as the group. It would also be interesting to track how these rates would vary among groups, and how easily a single user simultaneously part of multiple disconnected online social groups can switch between the different mores of implicit signalling of the groups.

[2] mentions that “sites like Twitter … feel ephemeral” because of continuous streams of content, despite not being ephemeral. Would this have an impact akin to ephemerality on the user? Would there be any 4chan like “bumping” (or subtler forms of such behavior) on Twitter, because the end user subconsciously feels the site to be ephemeral?

Side note: [2] mentions in passing that to combat the effect of ephemerality of data on 4chan, users comment “bump” or sometimes “bamp” (as a linguistic variant of “bump“). This highlighted for me the importance of spending a lot of time on the particular website or online community being explored, before conducting data analysis, as a casual user of 4chan would be aware of “bumping“, but not of the keyword “bamp“, which is something of an intermediate reference. Thus, posts which were “bamped” would be ignored while gathering data for analysis in this case.

[1] also states that Usenet went from being ephemeral to being a permanent website, as far as data storage was concerned. This raises an interesting opportunity to explore the effects of this transition on the usage patterns, and the behavior of users of the website.

[1] talks about how a users web home page is a useful, believable way of declaring identity, since it is time-consuming to make, harder to fake, and can’t be discarded or replicated easily. Thus, it increases the cost of deception. Most online users nowadays do not have personal web-pages, but many users connect certain website accounts (e.g. Goodreads, Instagram, etc.) to their Facebook or Google accounts. If these accounts are sufficiently old or regularly used, this greatly increases the cost of deception on a website where the users Facebook or Google account is linked, as they contain large amounts of important social information, and may also be linked to other websites. It would be interesting to explore user speech or behavior patterns on websites linked with their Facebook account, like the rudeness/politeness of speech, lies told, etc., versus websites where there is no need to link another social media account. 

 

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