Digifesto

Tag: weird twitter

Sample UC Berkeley School of Information Preliminary Exam

I’m in the PhD program at UC Berkeley’s School of Information. Today, I had to turn in my Preliminary Exam, a 24-hour open book, open note examination on the chosen subject areas of my coursework. I got to pick an exam committee of three faculty members, one for each area of speciality. My committee consisted of: Doug Tygar, examining me on Information System Design; John Chuang, the committee chair, examining me on Information Economics and Policy; and Coye Cheshire, examining me on Social Aspects of Information. Each asked me a question corresponding to their domain; generously, they targeted their questions at my interests.

In keeping with my personal policy of keeping my research open, and because I learned while taking the exam the unvielling of @horse_ebooks and couldn’t resist working it into the exam, and because maybe somebody enrolled in or thinking about applying for our PhD program might find it interesting, I’m posting my examination here (with some webifying of links).

At the time of this posting, I don’t yet know if I have passed.

1. Some e-mail spam detectors use statistical machine learning methods to continuously retrain a classifier based on user input (marking messages as spam or ham). These systems have been criticized for being vulnerable to mistraining by a skilled adversary who sends “tricky spam” that causes the classifier to be poisoned. Exam question: Propose tests that can determine how vulnerable a spam detector is to such manipulation. (Please limit your answer to two pages.)

Tests for classifier poisoning vulnerability in statistical spam filtering systems can consist of simulating particular attacks that would exploit these vulnerabilities. Many of these tests are described in Graham-Cumming, “Does Bayesian Poisoning exist?”, 2006 [pdf.gz], including:

  • For classifiers trained on a “natural” training data set D and a modified training data set D’ that has been generated to include more common words in messages labeled as spam, compare specificity, sensitivity, or more generally the ROC plots of each for performance. This simulates an attack that aims to increase the false positive rate by making words common to hammy messages be evaluated as spammy.
  • Same as above, but construct D’ to include many spam messages with unique words. This exploits a tendency in some Bayesian spam filters to measure the spamminess of a word by the percentage of spam messages that contain it. If successful, the attack dilutes the classifier’s sensitivity to spam over a variety of nonsense features, allowing more mundane spam to get through the filter as false negatives.

These two tests depend on increasing the number of spam messages in the data set in a way that strategically biases the classifier. This is the most common form of mistraining attack. Interestingly, these attacks assume that users will correctly label the poisoning messages as spam. So these attacks depend on weaknesses in the filter’s feature model and improper calibration to feature frequency.

A more devious attack of this kind would depend on deceiving the users of the filtering system to mislabel spam as ham or, more dramatically, acknowledge true ham that drives up the hamminess of features normally found in spam.

An example of an attack of this kind (though perhaps not intended as an attack per se) is @Horse_ebooks, a Twitter account that gained popularity while posting randomly chosen bits of prose and, only occasionally, links to purchase low quality self-help ebooks. Allegedly, it was originally a spam bot engaged in a poisoning/evasion attack, but developed a cult following who appreciated its absurdist poetic style. Its success (which only grew after the account was purchased by New York based performance artist Jacob Bakkila in 2011) inspired an imitative style of Twitter activity.

Assuming Twitter is retraining on this data, this behavior could be seen as a kind of poisoning attack, albeit by filter’s users against the system itself. Since it may benefit some Twitter users to have an inflated number of “followers” to project an exaggerated image of their own importance, it’s not clear whether it is in the interests of the users to assist in spam detection, or to sabotage it.

Whatever the interests involved, testing for this kind of vulnerability to this “tricky ham” attack can be conducted in a similar way to the other attacks: by padding the modified data set D’ with additional samples with abnormal statistical properties (e.g noisy words and syntax), this time labeled as ham, and comparing the classifiers along normal performance metrics.

2. Analytical models of cascading behavior in networks, e.g., threshold-based or contagion-based models, are well-suited for analyzing the social dynamics in open collaboration and peer production systems. Discuss.

Cascading behavior models are well-suited to modeling information and innovation diffusion over a network. They are well-suited to analyzing peer production systems to the extent that their dynamics consist of such diffusion over a non-trivial networks. This is the case when production is highly decentralized. Whether we see peer production as centralized or not depends largely on the scale of analysis.

Narrowing in, consider the problem of recruiting new participants to an ongoing collaboration around a particular digital good, such as an open source software product or free encyclopedia. We should expect the usual cascading models to be informative about the awareness and adoption of the good. But in most cases awareness and adoption are only necessary not sufficient conditions for active participation in production. This is because, for example, contribution may involve incurring additional costs and so be subject to different constraints than merely consuming or spreading the word about a digital good.

Though threshold and contagion models could be adapted to capture some of this reluctance through higher thresholds or lower contagion rates, these models fail to closely capture the dynamics of complex collaboration because they represent the cascading behavior as homogeneous. In many open collaborative projects, contributions (and the individual costs of providing them) are specialized. Recruited participants come equipped with their unique backgrounds. (von Krogh, G., Spaeth, S. & Lakhani, K. R. “Community, joining, and specialization in open source software innovation: a case study.” (2003)) So adapting behavior cascade models to this environment would require, at minimum, parameterization of per node capacities for project contribution. The participants in complex collaboration fulfil ecological niches more than they reflect the dynamics of large networked populations.

Furthermore, at the level of a closely collaborative on-line community, network structure is often trivial. Projects may be centralized around a mailing list, source code repository, or public forum that effectively makes the communication network a large clique of all participants. Cascading behavior models will not help with analysis of these cases.

On the other hand, if we zoom out to look at open collaboration as a decentralized process–say, of all open source software developers, or of distributed joke production on Weird Twitter–then network structure becomes important again, and the effects of diffusion may dominate the internal dynamics of innovation itself. Whether or not a software developer chooses to code in Python or Ruby, for example, may well depend on a threshold of the developer’s neighbors in a communication network. These choices allow for contagious adoption of new libraries and code.

We could imagine a distributed innovation system in which every node maintained its own repository of changes, some of which it developed on its own and others it adapted from its neighbors. Maybe the network of human innovators, each drawing from their experiences and skills while developing new ones in the company of others, is like this. This view highlights the emergent social behavior of open innovation, putting the technical architecture (which may affect network structure but could otherwise be considered exogenous) in the background. (See next exam question).

My opinion is that while cascading behavior models may in decentralized conditions capture important aspects of the dynamics of peer production, the basic models will fall short because they don’t consider the interdependence of behaviors. Digital products are often designed for penetration in different networks. For example, the choice of programming language in which to implement ones project influences its potential for early adoption and recruitment. Analytic modeling of these diffusion patterns with cascade models could gain from augmenting the model with representations of technical dependency.

3. Online communities present many challenges for governance and collective behavior, especially in common pool and peer-production contexts. Discuss the relative importance and role of both (1) site architectures and (2) emergent social behaviors in online common pool and/or peer-production contexts. Your answer should draw from more than one real-world example and make specific note of key theoretical perspectives to inform your response. Your response should take approximately 2 pages.

This question requires some unpacking. The sociotechnical systems we are discussing are composed of both technical architecture (often accessed as a web site, i.e. a “location” accessed through HTTP via a web browser) and human agents interacting socially with each other in a way mediated by the architecture (though not exclusively, c.f. Coleman’s work on in person meetings in hacker communities). If technology is “a man-made means to an end” (Heidegger, Question Concerning Technology), then we can ask of the technical architecture: which man, whose end? So questioning the roles of on-line architecture and emergent behaviors brings us to look at how the technology itself was the result of emergent social behavior of its architects. For we can consider “importance” from either the perspective of the users or that of the architects. These perspectives reflect different interests and so will have different standards for evaluating the importance of its components. (c.f. Habermas, Knowledge and Human Interests)

Let us consider socio-technical systems along a spectrum between two extremes. At one extreme are certain prominent systems–e.g. Yelp and Amazon cultivating common pools of reviews–for which the architects and the users are distinct. The site architecture is a means to the ends of the architects, effected through the stimulation of user activity.

Architects acting on users through technology

Drawing on Winner (“Do artifacts have politics?”), we can see that this socio-technical arrangement establishes a particular pattern of power and authority. Architects have direct control over the technology, which enables to the limits of its affordances user activity. Users can influence architects through the information their activity generates (often collected through the medium of the technical architecture itself), but have no direct coercive control. Rather, architects design the technology to motivate certain desirable activity using inter-user feedback mechanisms such as ways of expressing gratitude or comparing one’s performance with others. (see Cheshire and Antin, “The Social Psychological Effects of Feedback on the Production of Internet Information Pools”, 2008) In such a system, users can only gain control of their technical environment by exploiting vulnerabilities in the architecture in adversarial moves that looks a bit like security breaches. (See the first exam question for an example of user-driven information sabotage.) More likely, the vast majority of users will choose to free ride on any common pool resources made available and exit the system when inconvenienced, as the environment is ultimately a transactional one of service provider and consumer.

In these circumstances, it is only by design that social behaviors lead to peer production and common pools of resources. Technology, as an expression of the interests of the architects, plays a more important role than social emergence. To clarify the point, I’d argue that Facebook, despite hosting enormous amounts of social activity, does not enable significant peer production because its main design goals are to drive the creation of proprietary user data and ad clicks. Twitter, in contrast, has from the beginning been designed as a more open platform. The information shared on it is often less personal, so activity more easily crosses the boundary from private to public, enabling collective action (see Bimber et al., “Reconceptuaizing Collective Action in the Contemporary Media Environment”, 2005) It has facilitated (with varying consistency) the creation of third party clients, as well as applications that interact with its data but can be hosted as separate sites.

This open architecture is necessary but not sufficient for emergent common pool behavior. But the design for open possibilities is significant. It enables the development of novel, intersecting architectures to support the creation of new common pools. Taking Weird Twitter, framed as a peer production community for high quality tweets, as an example, we can see how the service Favstar (which aggregates and ranks tweets that have been highly “starred” and retweeted, and awards congratulatory tweets as prizes) provides historical reminders and relative rankings of tweet quality. Thereby facilitates a culture of production. Once formed, such a culture can spread and make use of other available architecture as well. Weird Twitter has inspired Twitter: The Comic, a Tumblr account illustrating “the greatest tweets of our generation.”

Consider another extreme case, the free software community that Kelty identifies as the recursive public. (Two Bits: The Cultural Significance of Free Software) In an idealized model, we could say that in this socio-technical system the architects and the users are the same.

Recursive public diagram

The artifacts of the recursive public have a different politics than those at the other end of our spectrum, because the coercive aspects of the architectural design are the consequences of the emergent social behavior of those affected by it. Consequently, technology created in this way is rarely restrictive of productive potential, but on the contrary is designed to further empower the collaborative communities that produced it. The history of Unix, Mozilla, Emacs, version control systems, issue tracking software, Wikimedia, and the rest can be read as the historical unfolding of the human interest in an alternative, emancipated form of production. Here, the emergent social behavior claims its importance over and above the particulars of the technology itself.

How to tell the story about why stories don’t matter

I’m thinking of taking this seminar because I’m running into the problem it addresses: how do you pick a theoretical lens for academic writing?

This is related to a conversation I’ve found myself in repeatedly over the past weeks. A friend who studied Rhetoric insists that the narrative and framing of history is more important than the events and facts. A philosopher friend minimizes the historical impact of increased volumes of “raw footage”, because ultimately it’s the framing that will matter.

Yesterday I had the privilege of attending Techraking III, a conference put on by the Center for Investigative Reporting with the generous support and presence of Google. It was a conference about data journalism. The popular sentiment within the conference was that data doesn’t matter unless it’s told with a story, a framing.

I find this troubling because while I pay attention to this world and the way it frames itself, I also read the tech biz press carefully, and it tells a very different narrative. Data is worth billions of dollars. Even data exhaust, the data fumes that come from your information processing factory, can be recycled into valuable insights. Data is there to be mined for value. And if you are particularly genius at it, you can build an expert system that acts on the data without needing interpretation. You build an information processing machine that acts according to mechanical principles that approximate statistical laws, and these machines are powerful.

As social scientists realize they need to be data scientists, and journalists realize they need to be data journalists, there seems to be in practice a tacit admission of the data-driven counter-narrative. This tacit approval is contradicted by the explicit rhetoric that glorifies interpretation and narrative over data.

This is an interesting kind of contradiction, as it takes place as much in the psyche of the data scientist as anywhere else. It’s like the mouth doesn’t know what the hand is doing. This is entirely possible since our minds aren’t actually that coherent to start with. But it does make the process of collaboratively interacting with others in the data science field super complicated.

All this comes to a head when the data we are talking about isn’t something simple like sensor data about the weather but rather is something like text, which is both data and narrative simulatenously. We intuitively see the potential of treating narrative as something to be treated mechanically, statistically. We certainly see the effects of this in our daily lives. This is what the most powerful organizations in the world do all the time.

The irony is that the interpretivists, who are so quick to deny technological determinism, are the ones who are most vulnerable to being blindsided by “what technology wants.” Humanities departments are being slowly phased out, their funding cut. Why? Do they have an explanation for this? If interpetation/framing were as efficacious as they claim, they would be philosopher kings. So their sociopolitical situation contradicts their own rhetoric and ideology. Meanwhile, journalists who would like to believe that it’s the story that matters are, for the sake of job security, being corralled into classes to learn CSS, the programming language that determines, mechanically, the logic of formatting and presentation.

Sadly, neither mechanists nor interpretivists have much of an interest in engaging this contradiction. This is because interpretivists chase funding by reinforcing the narrative that they are critically important, and the work of mechanists speaks for itself in corporate accounting (an uninterpretive field) without explanation. So this contradiction falls mainly into the laps of those coordinating interaction between tribes. Managers who need to communicate between engineering and marketing. University administrators who have to juggle the interests of humanities and sciences. The leadership of investigative reporting non-profits who need to justify themselves to savvy foundations and who are removed enough from particular skillsets to be flexible.

Mechnanized information processing is becoming the new epistemic center. (Forgive me:) the Google supercomputer approximating statistics has replaced Kantian trancendental reason as the grounds for bourgious understanding of the world. This is threatening, of course, to the plurality of perspectives that do not themselves internalize the logic of machine learning. Where machine intelligence has succeeded, then, it has been by juggling this multitude of perspectives (and frames) through automated, data-driven processes. Machine intelligence is not comprehensible to lay interpretivism. Interestingly, lay interpetivism isn’t comprehensible yet to machine intelligence–natural language processing has not yet advanced so far. It treats our communications like we treat ants in an ant farm: a blooming buzzing confusion of arbitrary quanta, fascinatingly complex for its patterns that we cannot see. And when it makes mistakes–and it does often–we feel its effects as a structural force beyond our control. A change in the user interface of Facebook that suddenly exposes drunken college photos to employers and abusive ex-lovers.

What theoretical frame is adequate to tell this story, the story that’s determining the shape of knowledge today? For Lyotard, the postmodern condition is one in which metanarratives about the organization of knowledge collapse and leave only politics, power, and language games. The postmodern condition has gotten us into our present condition: industrial machine intelligence presiding over interpretivists battling in paralogical language games. When the interpretivists strike back, it looks like hipsters or Weird Twitter–paralogy as a subculture of resistance that can’t even acknowledge its own role as resistance for fear of recuperation.

We need a new metanarrative to get out of this mess. But what kind of theory could possibly satisfy all these constituents?

Complications in Scholarly Hypertext

I’ve got a lot of questions about on-line academic publishing. A lot of this comes from career anxiety: I am not a very good academic because I don’t know how to write for academic conferences and journals. But I’m also coming from an industry that is totally eating the academy’s lunch when it comes to innovating and disseminating information. People within academia are increasingly feeling the disruptive pressure of alternative publication venues and formats, and moreover seeing the need for alternatives for the sake of the intellectual integrity of the whole enterprise. Open science, open data, reproducible research–these are keywords for new practices that are meant to restore confidence in science itself, in part by making it more accessible.

One manifestation of this trend is the transition of academic group blogs into academic quasi-journals or on-line magazines. I don’t know how common this is, but I recently had a fantastic experience of this writing for Ethnography Matters. Instead of going through an opaque and problematic academic review process, I worked with editor Rachelle Annechino to craft a piece about Weird Twitter that was appropriate for the edition and audience.

During the editing process, I tried to unload everything I had to say about Weird Twitter so that I could at last get past it. I don’t consider myself an ethnographer and I don’t want to write my dissertation of Weird Twitter. But Rachelle encouraged me to split off the pseudo-ethnographic section into a separate post, since the first half was more consistent with the Virtual Identity edition. (Interesting how the word “edition”, which has come to mean “all the copies of a specific issue of a newspaper”, in the digital context returns to its etymological roots as simply something published or produced (past participle)).

Which means I’m still left with the (impossible) task of doing an ethnography (something I’m not very well trained for) about Weird Twitter (which might not exist). Since I don’t want to violate the contextual integrity of Weird Twitter more than I already have, I’m reluctant to write about it in a non-Web-based medium.

This carries with it a number of challenges, not least of which is the reception on Twitter itself.

What my thesaurus and I do in the privacy of our home is our business and anyway entirely legal in the state of California. But I’ve come to realize that forced disclosure is an occupational hazard I need to learn to accept. What these remarks point to, though, is the tension between access to documents as data and access to documents as sources of information. The latter, as we know from Claude Shannon, requires an interpreter who can decode the language in which the information is written.

Expert language is a prison for knowledge and understanding. A prison for intellectually significant relationships. It is time to move beyond the institutional practices of triviledge

- Taylor and Saarinen, 1994, quoted in Kolb, 1997

Is it possible to get away from expert language in scholarly writing? Naively, one could ask experts to write everything “in plain English.” But that doesn’t do language justice: often (though certainly not always) new words express new concepts. Using a technical vocabulary fluently requires not just a thesaurus, but an actual understanding of the technical domain. I’ve been through the phase myself in which I thought I knew everything and so blamed anything written opaquely to me on obscurantism. Now I’m humbler and harder to understand.

What is so promising about hypertext as a scholarly medium is that it offers a solution to this problem. Wikipedia is successful because it directly links jargon to further content that explains it. Those with the necessary expertise to read something can get the intended meaning out of an article, and those that are confused by terminology can romp around learning things. Maybe they will come back to the original article later with an expanded understanding.

xkcd: The Problem with Wikipedia

Hypertext and hypertext-based reading practices are valuable for making ones work open and accessible. But it’s not clear how to combine these with scholarly conventions on referencing and citations. Just to take Ethnography Matters as an example, for my article I used in-line linking and where I got to it parenthetical bibliographic information. Contrast with Heather Ford’s article in the same edition, which has no links and a section at the end for academic references. The APA has rules for citing web resources within an academic paper. What’s not clear is how directly linking citations within an academic hypertext document should work.

One reason for lack of consensus around this issue is that citation formatting is a pain in the butt. For off-line documents, word processing software has provided myriad tools for streamlining bibliographic work. But for publishing academic work on the web, we write in markup languages or WYSIWIG editors.

Since standards on the web tend to evolve through “rough consensus and running code”, I expect we’ll see a standard for this sort of thing emerge when somebody builds a tool that makes it easy for them to follow. This leads me back to fantasizing about the Dissertron. This is a bit disturbing. As much as I’d like to get away from studying Weird Twitter, I see now that a Weird Twitter ethnography is the perfect test-bed for such a tool precisely because of the hostile scrutiny it would attract.

“Weird Twitter” art experiment method notes and observations

First, I got to say: Weird twitter definitely exists, and it is bigger and weirder than I imagined.

I want to write up some notes on my methodology for determining this, but I feel like some self-disclosure is in order.

I’m a PhD student with research interests that include community formation on the internet and collective intelligence. I’ve been studying theories about how communities establish their boundaries using symbols, and am also interested in “collective sensemaking.”

I am 27 years old and have been on the internet for long enough to know what I’m doing. I am really into conceptual art.

I’ve been aware of what I’ve referred to as “weird twitter” for some time, and have been curious what’s going on. I love it and love that it exists. But I didn’t know if it was real, or just something I was peripherally aware of because I followed a few people. It was much, much deeper than I had the patience to venture into at the time, but I had no sense of its scale.

Unfortunately, doing analysis on a gigantic unstructured digital social network turns out to be one of the big challenges of contemporary social science research. You either need to slurp a lot of data into something that crunches numbers, or you have to painstakingly research individuals in a tedious way that is not going to give you any general results on the scale necessary for this problem. So I tried a new method.

This method, which I don’t have a good name for, is basically: call it names, and see if it answers back. In other words, trolling.

I did this in August on a lark.

That post was an experiment.

  • Suppose “weird twitter” did not exist. Then there would be no reason for anybody to identify with its content. It would be a blog post lost in obscurity, like most of my blog posts.
  • But if “weird twitter” did exist, then there was a chance that it would react to its label in a statistically significant way.

I’ll adopt some speculative language for a moment: what if “weird twitter” were a kind of collective intelligence? Is it self-aware?

There are many theories of how self-awareness arises. Some believe that a person’s self-awareness depends on their interacting socially with others. It will not be a self until it is treated like a self. Until then, it will exist in some pre-conscious, animal state.

Others have argued that the Internet is creating a “global brain” of collective intelligence. This raises questions implicated by but far more interesting than the question of whether “corporations are people”. In what ways can a collection of people be a person? Do they need to self-identify as a community before that happens?

So many interesting questions.

Of course, if it were true that “weird twitter” were just a bunch of people telling jokes, and not a community, identity, culture, or collective intelligence, then a blog post about them would be meaningless and ephemeral.

For fun, I made the post extra obtuse.

I should say: “Weird twitter” seemed like a fun bunch, mostly just a bunch of jokers who don’t take things too seriously. So there was no way such a post would be taken seriously unless, well, I was wrong, and some people took it very, very seriously.

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I have never gotten more hate spam in my life. Holy crap.

It is a really good thing I have a thick skin, because the amount of abuse I’ve put up with in the past 48 hours has been intense. There has also been a pretty epic amount of disdain and even a little attempted character assassination….

A note about this:

Ok, I need to address this directly, partly because it is the sort of thing that can really ruin ones reputation, and partly because I think it raises some pretty interesting questions on feminism on the internet.

Kimmy (@aRealLiveGhost) is a talented poet whose work I generally like and have recommended to others who appreciate poetry. (Think her reconfigurations of @horse_ebooks tweets are her best work.) As far as I know, she got her start just tweeting authentically. At some point, she started posting pictures of herself along with her poetry. She also seemed alarmed by the number of followers she was getting.


That was in January, which was before she was a minor internet celebrity with thousands of followers. However, one source (see comments to this post) has noted considerable overlap between “weird twitter” and the feminist twitter landscape. In light of this whole art project/experiment thing, Kimmy referred to that tweet, which generated some discussion.

As I’ve learned, this comment bothered Kimmy, and I’ve apologized. As I’ve explained, my intention was to point out that there might be some connection between (especially a woman) posting cute pictures of herself on the internet and her suddenly getting a lot of attention on the internet. The recent Violentacrez scandal highlights the extremes of this, and why I might be concerned on her behalf.

This comment, which some have called “anti-woman”, has been variously interpreted as:

  • “mansplaining”, presumably because I should know already that all women on the internet know that putting cute pictures of themselves will get them a lot of attention/followers/whatever.
  • insinuating that Kimmy’s success (in terms of Twitter followers I guess?) is undeserved or only because she put pictures of herself on the internet.

I take feminism rather seriously and so I found these accusations pretty hurtful, actually. But then I thought about it and realized that taken together, they make no sense. So, I’m over it.

EDIT: In the ensuing discussion over this, I’ve learned a lot about how feminists think about this comment. It’s reasonable for women to suspect that somebody making such a comment has hostile or demeaning intentions, and that problem is especially exacerbated in low-bandwidth computer mediated communication such as Twitter, where so much is left to interpretation. I regret saying it.

In other words, the experiment was a wild success in terms of generating a significant reaction. However, the results coming in were literally all over the map: random hate, denial that the phenomenon existed, direct confirmation that the phenomenon existed, questioning of the meaning of it. A surprising number of people telling me I had “ruined” something, or “didn’t get” something.

Basically, there was every possible angle of existential crisis represented in the response from the collective consciousness of weird twitter.

Or maybe subconscious. Some people on Twitter seem to see it as primarily an expression of the subconscious. Which would explain why it hates getting called out so much.

These results were nonetheless inconclusive. Weird twitter was being awakened from its subconscious, unreflective slumber. So I gave it a kick.

This post was of course the kind of postmodern ironic half-joke that seems to be so characteristic of “weird twitter” but I guess it went over the heads of a lot of people.

There’s a legitimate concern here that this post involved what the academic and mainstream press has termed “cyberbullying”. But I made a calculated decision that people who were actively being dickish about the whole thing to me directly were asking for it and could handle being made fun of. In case anyone else was concerned (one person who contacted me was), I spoke with@bugbucket and @hellhomer and we’re cool.

The point of the second post was:

  • As a measurement instrument. I had a good indication that Weird Twitter really did exist. But how big is it? I’ve got analytics set up, and figured there was no reason for somebody who wasn’t part of weird twitter to want to read a post about weird twitter. This would give a rough order of magnitude estimate at least.
  • To test the theory that an on-line community exists partly by negotiating its own symbolic boundaries, and to see if it would achieve self-consciousness if pressed on the issue.
  • To generate more data about digital communities reacting to external reification. The nice thing about all this is that Twitter stores probably 99% of all the relevant communication for this kind of identity formation process (or the failure of it), so at some point somebody might dig it up and check it out in more detail.

In case you are wondering, if you were to ask me “How many people do you think are part of Weird Twitter?”, I’d now say “about 3,000″, if you operationalize “weird twitter” as “the number of people who care enough about being called out as Weird Twitter to read an article about it”. There may, of course, be multiple or overlapping weird twitters. Maybe other parts of the “weird twitter” landscape could be identified by referring to other patterns of behavior. (Maybe there’s a weird twitter that tells completely different jokes than the ones identified in the original post) Perhaps this only got to the most sensitive or curious bunch, those that actively click links. There’s also no accounting for factors like time zone.

Really the next thing to do would be to try to map out the actual social network structure.

Qualitatively, there were a lot of interesting reactions and questions raised in this process. I want to note them here before I forget:

  • Because of the tone of the initial post, I was estimated to be older than I am, and I got some criticism that I was some weird old guy invading somebody else’s space. One person, presumably a teenager, tweeting angrily that I was exploiting teenagers.

  • Lots of people reacted to the feeling of being watched or categorized. That’s ironic, because what people post on Twitter is openly available, and many of the members of this community of literally thousands of “followers”. And, Twitter data as a whole is being slurped and analyzed and categorized all the time algorithmically for research and marketing purposes. The amount of outrage created by a blog post that WASN’T based on observation of most of the system suggests that people in Weird Twitter really don’t get this.
  • One of the smartest response I saw was somebody who suggested making their posts more private to avoid having them looked at by people like me. Yes, that is correct. I was pulling a prank on you. I am the least of your problems.
  • Those who I guess you could call the “thought leaders” in the Weird Twitter community are experts at managing information flow. While several members of the community passed around links to my post directly, others were quite deliberate in posting links to images that would not be traced back here. My favorite posts were those that obliquely acknowledged there was a controversy going on with no navigable links at all.
  • I was definitely “othered” throughout the whole process, despite the fact that I’ve been using Twitter and interacting with a few of the members of this community in a peripheral way for a while, and the claims by some of its members that it’s just a community of people making jokes than anyone can join. (If it is the latter, then I declare myself a member.) Since its central members appear to have more followers than they can keep track of, it’s not surprising that they would see me as an outsider, especially given the estranged language and alternative platform of the blog post. @hellhomer‘s observations that I was unqualified to comment on the community because I only shared a small number of connections was evidence that online community membership can be operationalized as membership in a quasi-clique structure.
  • A lot of people assumed I’m planning on writing an academic article about this, and thought that would be exploitative. In reality, I think there’s no way in hell I would get this past the IRB. This was performance art. Y’all are suckers. Funniest were the people that got on my case about the flimsiness of my analysis or research methods. Funniest was the person that told me I really ought to be referencing Bruno Latour.
  • But, one day yeah maybe I’ll write an article about Weird Twitter. Obviously I’d go about it totally differently, though I might start with leads I’ve gotten through this project. I do believe that the best way to study radically transparent on-line communities is through radically transparent research (thanks Mel for introducing me to this term), which this experiment was an exercise in.
  • Who the hell posted this quora post on weird twitter? What’s their angle? Their insight that Weird Twitter is like the /b/ of Twitter is a bold claim, because fewer communities have had an impact on internet culture as great at /b/. Have any significant memes originated in Weird Twitter and escaped into the wild? Unclear. Are there other, similarly creative and unregulated pseudonymous communities in other social media?
  • I’ve been asked by one tweeter to ‘please explore the carefucker vs jokeman split amongst “weird twitter”‘. That is a useful research lead if I’ve ever seen one. “Carefucker” has not yet hit Urban Dictionary, but I guess the term is self-explanatory. Ironically, in my observations the most polished “jokemen” were also the most strategic and guarded about their references to being labeled, while the most authentically absurd appeared to be “carefucking”. I suspect that some folks were trying hard to be cool.
  • A significant portion of the reactions were people upset that I had “ruined” their “thing”, that thing which may or may not be weird twitter. If I had to guess, this is due to the perception that blog posts are less ephemeral than tweets, which is true, but also the illusion that what is phenomenologically ephemeral for them isn’t permanent in fact. As I said in my second post, there’s a weird power dynamic at work between blogs and tweets. But this is absurd. Because, if your attention span has been trained on blogs and not tweets, you realize that blog posts, too, are historically ephemeral. Most of the traffic to this post has been from Twitter itself. It is an artifact produced by Weird Twitter, not (as it has been accused of being) a voyeuristic or surveilling observation made on it from without. If this post has any significance within the history of that community, it will only be because the community’s consciousness of itself lead to a kind of dissolution (or suicide), or because its significance has outshone its containment within Twitter itself. Only time will tell on that one.
  • I have heard a lot of complaints about the prominence of internet trolls sending death threats to especially feminist bloggers. I find that really interesting, because I generally appreciate feminists and and do some research on internet security. It was pretty shocking how much vitriol I got exposed to for writing a blog post describing an internet community that maybe didn’t exist. I’ve assumed for the purpose of writing this that those people who attacked me were somehow motivated by anger at the blog post. But wouldn’t that be completely batshit? I mean, look at that first blog post. It’s dumb. I have an alternative theory, which is that there is a population on the internet that opportunistically hates on anybody who they think they can get away with hating on. This is a testable hypothesis, which if true would simplify the problem of cleaning up the mess. If hate speech on the internet were considered less a political issue and more an issue like spam detection and removal, I think the Internet would be a better place.
  • If you’ve read this far, then thank you for your interest. I’ve found this a very rewarding and insightful experience, and I hope you have gotten something out of it as well.

Weird Twitter: The Symbolic Construction of Community through Iterative Reification

Is there such a thing as Weird Twitter?

Earlier I wrote a blog post based on my (non-participant) observations of a Twitter subculture. Recently, there’s been some activity around it in the tweetosphere itself, which sheds light on how reification affects community development in social media.

This guy has almost 10,000 followers. Conversation analysis techniques indicate that he is upset at the reification of ‘weird twitter’ by an Other, ‘nerds’.

I have not yet been able to trace the reaction to these observations fully within Twitter itself. But preliminary results indicate discomfort within the alleged ‘weird twitter’ community as negotiates with its own boundaries in digital space.

Most of the reaction to the original post was dismissive (though I notice due to a sharp uptake in blog traffic that several people were intrigued enough to google for the post). But it just takes one stoned kid freaking out to escalate an irresponsible exaggeration of the truth into a reality.

This guy then began tweeting indignantly, apparently offended that I would refer to ‘weird twitter’ without being part of his social circle.

Twitter user @hell_homer (whose avatar depicts the popular Simpsons character, Homer, in hell) appears to not appreciate the irony that by reaching out to the people who might be concerned about the ‘weird twitter’ label, he is symbolically constructing the weird twitter community within the digital social space. @hell_homer autoreifies ‘weird twitter’ through his very acts of resistance.

He’s been going on like this now for like four hours.

Psychoanalytically, we might infer that @hell_homer’s suffers severe cognitive dissonance over his identification with “weird twitter”. Perhaps he identifies so strongly with weird twitter that he is offended by having the term appropriated by an outsider. Or perhaps he is concerned about his centrality with said ‘weird twitter’ community, and so seeks to embed himself further in it by taking responsibility for negotiation of its boundaries.

He persists in denial, weaving himself a cocoon of spite.

Other members of this community are willing to volunteer contact information about its central figures.

This suggests that ‘weird twitter’, rather than being a distributed social network, is rather more like a cult of personality, or personalities. Given the heavy-tail distribution of followers within Twitter and the immediacy of communication (no distance perceived by audience from “speaker”, even when the speaker has thousands of followers), this seems likely prima facie.

Others within ‘weird twitter’ react more violently to the application of the label:

Others became depressed:

…but also recognized, at least subliminally, the “threat” of having ones publicly facing community whose members have tens of thousands of followers “discovered” by internet media:

Is it the threat of exposure that is threatening? Or is it reifying gaze that comes with it? And how is that gaze constructed within the community as it is observed?

Here we see a single act of observation abstracted into “people” who want to categorize “every” community on the internet. The initially dismissed ‘hogwash’ has become, through the symbolic construction of ‘weird twitter’ itself, a surveilling conspiracy, placed firmly in opposition.

This user then proceeded to tweet a piece of microfiction prophesying the future of his community.

Speculation: as an earlier and more persistent mode of internet discourse, blogs are viewed by digital natives who primarily use Twitter as a social networking platform as a matured, out-of-touch, and marginally more socially powerful force. Moreover, academic language’s distinction from the vernacular echos the power dynamics of meatspace into the digitally dual virtual world. These power dynamics problematize organic community growth.

Field notes and PSA: Weird Twitter

For some time I’ve been following an emerging subculture on Twitter. I have referred to it occasionally as “stoner Twitter poets”, but as it attains consciousness of itself as a phenomenon, it has given itself a name: weird twitter.

Weird twitter posts tend to be of the following forms:

  • A brutally sincere statement of personal perspective, often with philosophical and spiritual sentiment, but just as often profane
  • nounal phrases referring to surreal compositions of objects
  • “sext:” followed by a declaration of attraction that is often only peripherally erotic (to humorous effect)
  • norm-building posts on appropriate twitter behavior, the state of weird twitter, and discussion of ‘favstars’

Interestingly, while Facebook “Likes” are generally derided, the “weird twitter” community holds the “favstar” in high esteem, as are pyramids and bots (especially ‘spam’ bots, like Horse Ebooks, which are used as source material for aleatoric poetry). And, though romance and attraction are frequently discuessed, “weird twitter” discourse is almost completely devoid of explicit sexual content, despite its otherwise transgressive qualities.

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