Tag Archives: twitter

Why do brands lose their chill? How bots, algorithms, and humans can work together on social media

Note from the Editor, Tricia Wang: The fourth contributor to the Co-designing with machines edition is Molly Templeton (@mollymeme), digital and social media expert, Director of Social Media at Everybody at Once, and one of the internet’s first breakaway YouTube stars. Her piece urges brands’ social media strategy to look beyond the numbers when working in the digital entertainment and marketing industry. Molly gives specific examples where algorithms don’t know how to parse tweets by humans that are coded with multiple layers of emotional and cultural meaning. She offers the industry a new way to balance the emotional labor in audience management with data analysis. Her articles draws on her work at Everybody at Once, a consultancy that specializes in audience development and social strategy for media, entertainment, and sports.

@Tacobell spent an hour sending this same gif out to dozens of people. The account is probably run by humans (most social media presences today are). And they were following best practice by “replicating community behavior,” that is, talking the way normal people talk to each other (a human taco bell fan would definitely send a gif). But when @tacobell only sends the same gifs out over and over again, it’s uncanny. It’s pulling the right answers from the playbook, but at the wrong frequency.  

Why do brands lose their chill?

I think that brands lose their chill when they don’t let their social media managers exercise empathy. The best brands on social media balance the benefit of interaction with the risk of human error – managers are constantly concerned with pissing off the organization, or the audience, and ultimately trying to please both sets of real people. Hitting campaign goals and maximizing efficiency are important, but social media managers need to bring humanity to their work. They have to understand the audience’s moods and where they’re coming from, and they have to exercise empathy at every level: customer service, information and content sharing, community management, call-to-actions, participation campaigns, crisis and abuse management. That is a lot of emotional labor.   

With the recent chatter about chat bots on Facebook’s messenger platform, a lot of people are thinking about how bots can take over communications roles from humans. I’ve been thinking a lot about the opposite: how can machines help people manage the emotional labor of working with audiences? Can bots ever help with the difficult, and very human task of managing with empathy?  

Social media is a business of empathy  

Emotional connections drive social media. When people gather around the things they feel passionate about, they create energy. It’s because of limbic resonance — the deep, neurological response humans have to other people’s emotions. As my colleague Kenyatta Cheese says, it’s that energy that makes participating as a fan on social media feel as electric as it does when you’re part of a physical crowd.  Read More… Why do brands lose their chill? How bots, algorithms, and humans can work together on social media

Why Weird Twitter

tl;dr There’s no such thing as Weird Twitter

Will there be a mythology in the future, they used to ask, after all has become science? Will high deeds be told in epic, or only in computer code?

And after the questing spirit had gone into overdrive during the early Space Decades, after the great Captains had appeared, there did grow up a mythos through which to view the deeds. This myth filter was necessary. The ship logs could not tell it rightly nor could any flatfooted prose. And the deeds were too bright to be viewed direct. They could only be sung by a bard gone blind from viewing suns that were suns.

R.A. Lafferty, Space Chantey

Imagine that there is a community or culture of people that use social media–let’s focus on Twitter–in a particularly interesting or funny or outlandish way. Would you give it a name? Would you try to understand its size or its structure? Its history? Its purpose? How would you go about doing that?

Could it be studied by an anthropologist? A data scientist? An economist? A philosopher? A critic? A journalist? Could it ever understand itself?

I’m getting ahead of myself. Let’s start with a name: Weird Twitter.


This is a Google Trends query for the string “weird twitter”.

There have been two spikes in search popularity of “weird twitter” in the past year. The second spike corresponds to the publication of the brilliant “Weird Twitter: The Oral History“, by Herrman and Notopoulos, (BuzzFeed, April 2013). The authors explain:

Weird Twitter is vast and amorphous; what it looks like depends hugely on whom you follow, when you followed them, and what you find funny. … Some of its best writers have a few hundred followers, while others have tens of thousands. Styles of tweeting and types of jokes that originated among its small sects have bled out into the mainstream: Even to comedians, these are some of the funniest people on Twitter.

The first spike in October 2012, which marks the beginning of a consistent hum of search activity for “weird twitter”, coincides with this tweet by erstwhile pseudonymous Gawker writer Mebute Sese Seko.

At the time, Mebuto Sese Seko had almost 10,000 followers. This tweet triggered a series of events that lead to the wider adoption of the term.

Notably, Mebuto didn’t use the phrase “weird twitter” himself, and he linked to screenshots, not to web pages. The second image was of an anonymous Quora post that identified Weird Twitter as Twitter’s equivalent of 4chan’s /b/, or “Random”, subcommunity. The first image pictured a blog post I had written the previous summer.

Boundaried symbolic network community

A lot was going through my head when I wrote that blog post. In the Spring of 2012 I was reading Anthony Cohen‘s work on the symbolic construction of community. For Cohen, a community is constituted by its creation and use of symbols. Especially critical for the community’s identity are the symbols it uses to mark its boundary–members and non-members. I was also reading Caroline Haythornthwaithe‘s work on Social Networks and On-line Community, which emphasizes the topological structure of on-line social networks over symbolic meaning-making.

Schematic diagrams of Cohen and Haythornthwaithe's models of community.

(I did this preliminary work in on-line community detection in collaboration with a classmate, Dave Tomcik.)

At the same time I was following a few of what now would be called Weird Twitter accounts. It got me thinking: most work on virtual communities in cyberspace depends on technical infrastructure to provide the community boundaries. A mailing list is a community circumscribed by the technicalities of mailing list membership. A web service like Reddit supports multiple communities by supporting multiple distinct subreddits. But Twitter supports the growth of an ad hoc network structure without distinct watering holes demarcated in the user interface.

How could one identify a community within such a social network? I had a hunch that these networks, which would depend on the organic social connections between individuals and not the commercially built and sustained technical environment, would be special.

Schematic diagram of a boundaried symbolic networks and containment relations between its lexicons

What sort of digital signature would such a community show? According to my reading of Cohen, it would be involved in vigorous, complex dialog about, among other things, what symbols to use to represent itself. Since the digital environment is one in which symbols (e.g. words) are constantly flying around and being recorded, I thought this kind of community could be algorithmically detected. Which I find both thrilling and chilling. Eric Snowden’s recent whistleblowing has let America know its on-line activity is under state surveillance all the time, on top of the surveillance by commercial interests.

Chaotic resistance

Duchamp's FountainI believe we have an opportunity to use the wealth of data available now to really advance social science. But the reality is our research will, if successful, be used for political manipulation, commercial advertising, and other kinds of social manipulation and control. For me, this makes it imperative that I present my research to the public. If I work on methods for on-line community detection, I should try to make those insights usable by communities to understand themselves and evade detection if they desire.

Most rhetoric about evading on-line detection is about making less information available. That makes sense for the individual. But in aggregate, this cleans the data set, making it easier to find patterns that haven’t been self-censored away.

The biggest challenges to behavioral data scientists are not the availability of data, but data’s complexity. If data has a high Kolmogorov complexity and perhaps logical depth, it will be very difficult to extract patterns from.

In other words: you cannot master noise and chaos. It is the abyss staring back. Much as Dadaism was a tool for eroding the establishment and Situationists sought to challenge society through liberated, authentic expression, social media users can resist surveillance by making their interactions more wild, original and complex. Big Brother is watching, but he can be blinded by confusion fu.

We are always already watched over by machines of love and grace, and other kindsI thought I had found a nexus of this kind of noisy on-line behavior on Twitter. If what I was seeing was a community at all, it was a community of chaos and exploration. And it knew it. I had read the term “weird twitter” in @regisl‘s tweets in March 2012 when he was writing a lot of exploratory, reflective thoughts on Twitter culture. His and others insights into virtual community were profound, echoes of some of the earliest musings on virtual community, such as by Electronic Frontier Foundation founder John Perry Barlow in “Crime and Puzzlement” (1990):

As a result of [the opening of Cyberspace], humanity is now undergoing the most profound transformation of its history. Coming into the Virtual World, we inhabit Information. Indeed, we become Information. Thought is embodied and the Flesh is made Word. It’s weird as hell.

I figured that holding a mirror up to the noise, crudely describing and interpreting practices that could not be interpreted or described, could only make the chaotic system more complex. At the same time, it would be a test of Cohen’s theory of the symbolic construction of communities in an on-line context: what happens when a community confronts a symbol, in this case the string “Weird Twitter”, that purports to mark its boundary?

Stuart Geiger had introduced me to M.C. Burton’s idea that “trolling is the new critique.” I was interested in trolling as an experimental method. A grown-up Internet kid who had done and been dished his share of trolling, I figured it was time to put those skills to good use: clumsy live field notes.

The offending post

According to the Encyclopedia Dramatica, this kind of trolling is a Philosopher Attack (“a type of flame war
where a terminally bored, yet well educated person or group ambush an innocent bystander or group, who were just minding their own business”). I posted in August. Nothing happened.

Then Jeb Lund tweeted about it.

Read More… Why Weird Twitter

“The @Adderall_RX Girl”: Pharmaceutical self-branding and identity in social media

headshot of Tazin Karim

Tazin Karim

Editor’s Note:  Tazin Karim (@PharmaCulture) is a medical anthropologist who studies pharmaceutical culture in the US and contexts of prescription stimulant use.  She is also active in the Digital Humanities and Social Sciences. In this post for our Virtual Identity edition, Taz examines the ways in which people use Twitter to construct virtual identities centered on the brand name stimulant Adderall.


In today’s digital world, choosing the right Twitter username is an important decision. It’s the first thing people notice and immediately signals to a potential follower who you are and why they should be interested in what you have to say. Although many stick to their given names, others use the opportunity to highlight their best qualities and brand themselves as an expert academic, baseball fanatic, or mother of the year. So when I found out there were over a hundred people on Twitter with the word “Adderall” in their username, it definitely got my attention. Of all the things to advertise, why would someone want to brand themselves around a mental health drug?

Adderall is a prescription stimulant designed to treat the symptoms of Attention Deficit and Hyperactivity Disorder (ADHD) – a condition affecting 12% of children and 5% of adults in the U.S. It is also used non-medically by a number of people from middle aged mothers to professional football players looking to manage their high-stress lives. My research in particular looks at the popularity of Adderall use among college students and how it is influencing cultural conceptions of mental health and academic performance.

Like other prescription drugs, the consumption of Adderall has become an important part of identity construction for many Americans. For a person with ADHD, it acts to reify the sick role by offering a tangible solution to an illness that is difficult to biomedically conceptualize. Lay conceptions of ADHD extend beyond biomedicine and are intimately tied to academic culture (“my grades are poor because I have ADHD” or “his grades are poor, he must have ADHD”). As a result, Adderall consumption can also construct and facilitate non-medical identities like being a good student, son/daughter, athlete, or friend. As the prevalence of these pharmaceutical practices increases, Adderall use is becoming not only de-stigmatized in American culture, but a normalized, and even glamorized way to achieve these idealized identities – both off and online.Read More… “The @Adderall_RX Girl”: Pharmaceutical self-branding and identity in social media

Tweeting Minarets: A personal perspective of joining methodologies

David Ayman Shamma

Editor’s note: In the last post of the Ethnomining‘ edition, David Ayman Shamma @ayman gives a personal perspective on mixed methods. Based on the example of data produced by people of Egypt who stood up against then Egyptian president and his party in 2011, he advocates for a comprehensive approach for data analysis beyond the “Big Data vs the World” situation we seem to have reached. In doing so, his perspective complements the previous posts by showing the richness of ethnographic data in order to deepen quantitative findings.
David Ayman Shamma is a research scientist in the Internet Experiences group at Yahoo! Research for which he designs and evaluate systems for multimedia-mediated communication.


There’s a problem we face now; the so called Big Data world created an overshadowing world of numerical data analysis leaving everyone else to try to find a coined niche like “small data” or “long data” or “sideways data” or the like. The silos and fragmentation is overwhelming. But really, it’s just all data. Regardless of the its form or flavor, there are people who are experts at number crunching data and people who are experts at field work data. Unfortunately, the speed at which data science moves is attractive and that’s part of the problem; we don’t get the full picture at speed and everyone is racing to produce answers first.

A few months ago, in a conversation with a colleague, he told me “you don’t know what you don’t know, especially when it’s not there.” We were looking for a way to automatically surface a community of photographers on Flickr who didn’t annotate their photos. They didn’t use any titles or tags or any annotations what so ever. But they were clearly a strong and prolific community. If there was some way to automatically identify them, then we could help connect them.

Now, finding metrics for social engagement in unannotated data is not an impossible task when provided with some signal in the data that has some correlation, statistical or otherwise, to the effect you’re trying to surface. But in some cases, it’s just not possible. What you need is just not there; therein is a problem. In other cases, it’s much harder to surface features when you don’t know what they look like.

When you have a lot of data, finding that unexplainable prediction through algorithmic statistics becomes easier. It doesn’t explain why and it doesn’t always work.

Enter Ethnography to answer the why and find out what things might look like—surfacing findings in the age of big data. When I was invited to write a post on Ethnography Matters, I decided to illustrate this through a personally motivated example.

In the late January of 2011, the people of Egypt stood up against then President Hosni Mubarak and his National Democratic Party. They wanted employment, a fair government, and an end to the 30 year long emergency law which had removed most of their civilian rights. Undoubtedly, you read about it somewhere. At the time, my mother was in Cairo visiting her 100+ year old mother. So this left me glued to the only source of news I could find—a rather buggy Al Jazeera video stream. U.S. news agencies were slow to start some sparse coverage. Somewhere in-between, it was burning up on Twitter.

Tharir tweets

A visualization of Twitter activity directed towards Tahrir by aymanshamma

Read More… Tweeting Minarets: A personal perspective of joining methodologies

Why everyone loves Bieber

screen capture of tweets containing "everyonelovesbieber"

It’s Bieber’s world; we’re just living in it.

In an illustration of the socio-technical gap, people[1] mostly consider Occupy Wall Street a trending topic, but Twitter’s algorithms mostly do not.

Amid rumors that Twitter is suppressing #occupy tags from trending, Gilad Lotan looked at data on tweets containing occupy-related terms and on occupy-related trending topics since September 25th.  In Lotan’s analysis, trending topics require a spike in the rate of activity, rather than a slow and steady increase in volume. #OccupyWallStreet, in Lotan’s example, was never a trending topic in New York where the action started. Instead, it first broke through as  a trending topic in Madrid.

Read More… Why everyone loves Bieber