Wednesday, January 18, 2017

The Partial Truths of Big Data


Last July I was using R to do some social network analysis of Instagram tags.  After lots of package downloads, App Developer’s applications, etc., I couldn’t get it to work, only to discover that Instagram had changed its policy the months before.  Like many social media platforms, Instagram had restricted access to data through its API (Application Programming Interface).  For some, this could be welcome news—after all, third party developers having untrammeled access weakens privacy and serves to expose more and more of our lives to commodification.

But this isn’t the whole story.  Just because I (a researcher at a mid-tier state university) was having trouble gaining access doesn’t mean that large corporations were having trouble, or the National Security Agency, or Instagram itself.  Rather, what we’ve seen with the rise of Big Data as a research object is the progressive commodification of social media.  The social network analysis that began as a recondite branch of anthropology, sociology and mathematics has become an indispensable tool in business development.  Social media data are money, and the tightening of restrictions represents another digital divide, this one between corporations and governments that can gain access to the “firehose” of complete data, while the rest of us work with a fraction of that under whatever restrictions are placed upon data access through APIs.  In this latest chapter of the digital divide, some people (and entities) get Big Data, and some of us get “partial” data.

This has prompted some scholars to question the involvement of academics in Big Data analysis in the first place: “How much of a difference does it make for academics to gain access to Big Data, after all, when the logics of commercial enclosure of social media data may [have] already begun to run deep?” (Chan 2015: 1080).  It certainly doesn’t look good for cultural anthropologists—our “n” in a research study rarely exceeds one hundred.  Compare that to the 2016 update of a 2011 study from Facebook that looks to social distance and weak ties among its 1.5 billion users, concluding the geodesic distance between anyone on the planet is about 3.57 “degrees of separation” (Bhagat et al 2016).  It would be hard for anthropology to compare their work to this.  And yet, as Tricia Wang (2013) has reminded ethnographers, we have little choice but to work with the Big Data science around us: “Otherwise our work will be all too easily shoved into another department, minimized as a small line item in a budget, and relegated to a small data corner” (Wang 2013).  One strategy here is to point out the obvious.  “Big Data” (however construed) does not interpret itself; it needs context, theory, narrative—in other words, the work of anthropology.  In their often cited 2012 paper, dana Boyd and Kate Crawford urge researchers to critically engage the emergent hegemony of Big Data by pointing to the limits of the data these social media platforms aggregate.  “Do numbers speak for themselves?  We believe the answer is ‘no’” (boyd and Crawford 2012: 666).

But this means more than stressing the importance of history and political economy to the quanta of data we emit.  We need to ask more subversive questions.  What kinds of numbers are generated in the space of social media?  What, for example, does Facebook know about me?  On the one hand, it undoubtedly knows a great deal.  Not only am I updating Facebook with personal information (photos from family trips, political opinions), but I’m also “liking” groups, causes, music, etc. on Facebook and, furthermore, Facebook harvests cookies from my non-Facebook internet perambulations in order to “better serve me” advertising targeted to my demographic and political leanings.

But none of this, I would suggest, is really “anthropological” data—instead, it’s consumer data, information about what I buy, and what I might be tempted to buy.   It’s tempting to leap from this to insights into culture, society and social action, but that’s not really what Facebook is collecting.   The numbers are numbers about consumers—users who click on links, who link to each other, who can be profiled in order to sell more.  When we do other things on Facebook: “like” a group or respond to efforts to organize for a cause, we do so through a consumption frame.  Not surprisingly, this has led to several critiques of slacktivism: it looks like consumption without a credit card number.  In any case, Facebook data is not, as Boellsstorff put it, “raw data.”  Instead—it’s already been thoroughly “cooked”, data as emanating from an individual consumer (Boellstorff 2013).  

As far as Facebook is concerned, though, this is all that’s important.  Facebook thinks it knows the whole truth, and, from the perspective of an enormous, monopolistic corporation, it knows all it needs (or cares) to know about my identity, habits and social relations.  And yet, it does not.  The emergent, the collective, the alternative, the subaltern, becoming-animal, the multitude—Facebook will never start the revolution, because Facebook can only know our social lives through the reified perspective of commodification.  Of course, activists have utilized Facebook (and other social media) for their work, but they do this in spite of the platforms themselves, media frames that will gamely struggle to track shopping and supply advertising to even the most ardent revolutionary’s account.  Big Data, then, is always “partial” data.

In other words, Facebook (and other social media) disclose “partial truths.”  I deploy this term from Clifford’s often-cited (and often excoriated) introductory essay to “Writing Culture,” a collection of essays that is widely credited with issuing in anthropology’s “postmodern” age.  There, Clifford (1986: 10) focuses attention on the ways ethnographic accounts “construct” culture and, in particular, the ways these genre conventions both enable and delimit anthropological truth:
“Cultures” do not hold still for their portraits.  Attempts to make them do so always involve simplification and exclusion, selection of a temporal focus, the construction of a particular self-other relationship, and the imposition of a power relationship.
In focusing on the constructedness of the ethnographic encounter, Clifford led a generation of anthropologists to experiment with the ethnographic form and to reflect on their dyadic, field encounters.  But by directing our attention to the dyadic encounter, he deflects our attention from other contexts, among them political economy, social activism, postcolonial struggle and the work of the different communities in which anthropologists site their work.  As many critics have since concluded, anthropology is only in the last (and reified) instance, the ethnographic representation of a dyadic encounter.

There is, nevertheless, truth in Clifford, but it is a truth that serves to conceal other truths.  As Taussig writes of magic in general, “The real skill of the practitioner lies not in skilled concealment but in the skilled revelation of skilled concealment” (Taussig 2003:273).  A momentary glimpse into one secret serves to conceal another; for anthropology, the truth of ethnography served to conceal the onslaught of neo-liberalism.  This is where we can re-define Clifford’s titular perspective: not just a “part,” and not just biased, but a truth that obscures other truths.

With Big Data, the magic is the same.  There are truths to Big Data, but the focus upon them obscures other insights that may lead us to critical alternatives.  The same theories and methods that graph connected action and aggregate millions of data points also serve to deflect the eye from local process, or from action that unfolds over a longer timeline, or non-episodic phenomena that continue without defining “events”.    

In his 2011 book, Rob Nixon introduced the concept of “slow violence,” “a violence that occurs gradually and out of sight, a violence of delayed destruction that is dispersed across time and space, an attritional violence that is typically not viewed as violence at all” (2).  Ordinary violence—along with other temporally discrete phenomena—is particularly amenable to social media.  How many examples of police violence, for example, have been rendered visible through their felicitous recording on smartphones, the resulting videos uploaded to Facebook?  But slow violence proceeds without these—incremental tragedy impacting health, education and psychology.  Nixon concentrates his analysis on the slow violence of environmental degradation, and, particularly, on the ways that marginalized communities suffer through policies that enable corporations and governments to concentrate pollution in communities that cannot defend against it.  But slow violence can take many other forms, including processes of structural violence, de-industrialization, de-funding, under-development, infrastructure decay, pathologization.  None of these may spark social media storms, but these “slow” processes have the same, calamitous consequences in neighborhoods in both urban and rural areas.

This is where the data of anthropology and the “Big Data” available through social network analysis seem to diverge the most, but the onus is upon us to attempt to identify the lacunae and, when possible, use our methodological understandings to move in these interstices.  And it can mean using Big Data in ways contrary to the social media platforms that aggregated it in the first place—e.g., researching food deserts through Instagram (Beck 2016).  It is, however, not an easy task to take images that reflect the commodification of daily life and the drive towards the “quantified self” and appropriate them to advance social justice.  And it is here where the ethnography that seemed so beside the point suddenly becomes vital.

References

Beck, Julie (2016).  “The Instagrams of Food Deserts.”  The Atlantic [accessed on November 1, 2016 at www.theatlantic.com].

Chan, Anita (2015).  “Big data interfaces and the problem of inclusion.”  Media, Culture & Society: 1080-1086.

Bhagat, Smriti, Moira Burke, Carlos Diuk, Ismail Filiz and Sergey Edunov  (2016).  “Three and a half degrees of separation.”  Facebook Research [retrieved from research.fb.com on November 10, 2016].

Boellstorff, Tom (2013).  “Making big data, in theory.”  First Monday 18(10).  [Retrieved at firstmonday.org on January 6, 2017].

boyd, dana and Kate Crawford (2012).  “Critical Questions for Big Data.”  Information, Communication & Society 15(5): 662-679.

Clifford, James (1986).  “Partial Truths.” In Writing Culture, ed. By James Clifford and George Marcus.  Berkeley: University of California Press.

Nixon, Rob (2011).  Slow Violence and the Environmentalism of the Poor.  Cambridge: Harvard University Press.

Taussig, Michael (2003) “Viscerality, Faith, and Skepticism.”  In Magic and Modernity, ed. By Birgit Meyer and Peter Pels, pp. 272-306.  Stanford: Stanford University Press.

Wang, Tricia (2013).  “Big Data Needs Thick Data.”  Ethnography Matters [retrieved from ethnographymatters.net on September 3, 2013].


Tuesday, November 22, 2016

#AMANTH2016 WRAP-UP

The American Anthropological Association Annual Meeting is over, and, with it, the brief spurt of Twitter traffic that marks the event.  Here's a graph of Twitter traffic over the course of the week, created on NodeXL through a Twitter search for the hashtag #amanth2016:




And some statistics on the graph:

Vertices: 1746    
Unique Edges: 4090
Edges With Duplicates: 6825
Total Edges: 10915

Here are the 50 most popular twitter accounts by betweenness centrality:

americananthro
culanth
biellacoleman
omanreagan
thevelvetdays
cmcgranahan
aba_aaa
berghahnanthro
ericalwilliams7
allergyphd
michelleakline
amreese07
jasonantrosio
fatimatassadiq
anthroboycott
peepsforum
anthrofuentes
teachingculture
hilaryagro
aaa_cfhr
dukepress
afeministanthro
anandspandian
anthrocharya
aunpalmquist
shahnafisa
jahkarta
nolan_kline
elena_sesma
savageminds
stanfordpress
girlhoodstudies
transformanthro
drkillgrove
anthronad
globalsportuva
ruthbehar
kimjunelewis
hacrln
nycnodapl
amethno
salliehananthro
beliso_dejesus
mounia_elk
angelacjenks
apv2600
melaniesindelar
jessacabeza

And, a wordcloud showing the most prominent word pairs:


(from wordclouds.com)

And from this list, some of the most prominent keywords (excluding personal names and Twitter usernames).

NoDAPL (North Dakota Pipeline)



White supremacy

Trump


As in other years, anthropologists tweet their sub-specialties and interest groups, but are likely to re-tweet areas of broad interest that cut across anthropology.  Current concerns about growing fascism, white supremacy in the U.S. together with (related) violence against Native American protestors in cut across interest groups and energize discourse between anthropologists who might normally remain siloed in their own sub-groups. In the graph, these tweets provide connections between clusters.  As in previous years, I note that these moments allow anthropologists to "perform anthropology" at the Annual Meeting and, simultaneously, create moments of coherency across a large and fragmented group of academicians, practitioners and students.

And, yet, last year's AAA spawned twice as much activity as this year's--testament, perhaps, to a decline in attendance this year (although I have not seen an official count) and to continued confusion over hashtagging.  

Thursday, November 17, 2016

Hashtag Chaos: #AAA2016 vs. #amanth2016

As I've done over the past 3 years, I ran Twitter searches for the American Anthropological Association Annual meeting this evening.  Unlike previous years, though, the #AAA2016 hashtag seems to be popular with a number of different groups, effectively obfuscating the anthropological voice behind other causes.  My colleague Matthew Durington (@mdurington) noticed that and tweeted yesterday:


But, really, much of the damage had been done.  Here's a graph of my search results for #AAA2016:


This dense graph (over 10000 edges) is made up of tweets from several events, but it's dominated by one, the "Asian Artists Awards" held in Seoul.  Here's the top tweet by betweenness centrality:

1. "คนอะไรเก่งตั้งแต่เด็ก และเก่งขึ้นเรื่อยๆ แถมสวยน่ารักขึ้นทุกปี

#kimyoojung #AAA2016 https://t.co/DZVCDlnsY5"

Indeed, all of the connected components are tweets from the Asian Artist Awards, with the twitterverse dominated by Thai fans.  The American Anthropological Association, on the other hand, is relegated to the violet component on the far, middle-right of the graph.

Changing my search to the new hashtag, "#amanth2016", we get a much smaller graph with a few hundred edges:  


Here, the top tweets are from @AmericanAnthro, @JasonAntrosio, @culanth and @mdurington, and they revolve around the Melissa Harris Parry address on Wednesday night, the usual announcements of book vendors and paper presentations, and some self-referential discussion of the hashtag confusion itself.  

So, what's the lesson?  There's a lesson for marketing in here, certainly, but also a broader and more obvious one for the American Anthropological Association: know what's happening in the world you purport to study.  After all, there was the same collision of hashtags last year (the Asian Achievement Awards and the Act Against AIDS concert in Japan), albeit not the complete eclipse the music awards has meant for anthropological twitter content.  More than anyone else, we should know that we join a crowded field of representation, and that the digital content we produce may flow into networked connections we never intended.  If we fail to attend to these other sources of digital content and meaning, then anthropological voices are effectively silenced. 

Saturday, November 12, 2016

Multimodality Through Twitter: Exploring the Alleyways of Seoul


Multimodality describes an anthropology across multiple media platforms--an anthropology that traverses film, photograph, theater, design, podcast, app and game (to name a few) as well as conventional modes of print representation.  But multimodality is a restless, protean concept, one that has already exploded its initial demarcation (modes of dissemination) into a spectrum of engagements.  Multimodality is about the platforms we use as we produce our work (social media, blogs, websites), and the social media that ripples out from it as people share, comment, re-mix and appropriate.  Finally, multimodality is the acknowledgement that people are engaged in anthropologies of their own lives, and that these productions (YouTube videos, Instagram photos) are worthwhile of attention as ethnographically intended media in their own rights.  By multimodality, then, we re-cognize anthropology along 2 complementary axes--a horizontal one that links together phases of ethnographic work that are oftentimes held distinct from each other, and a vertical one that links our anthropological work to the anthropologies of our collaborators.  Moreover, with the development of new media, new media platforms, and new forms of collaborative work, we would expect these axes to multiply.   Ultimately, multimodality takes the arbitrary divisions we make in our work and in our collaborations to task, and offers up new possibilities for old dilemmas.

Many visual anthropologists are, of course, multi-modal avant la lettre: their finished, ethnographic film is preceded by countless edits, photographic stills and recorded interviews.  But all of us are multimodal anthropologists; in an age of social media, anthropologists spread ethnographic filaments through multiple platforms before they engage in the "real" writing on their print monographs and journals.  At this level, multimodal research means tracing this arc through social media before, during and after ethnographic research.  


A short example:

In 2014-2015, I was doing research in Seoul on representations of the city through both mass media (novels, film, web comics) and through social media, particularly social media produced by neighborhood groups affiliated with various "village media" (마을 미디어) projects.  In particular, I was attracted to social media postings of alleyways and narrow roads, photos that looked like they were from the 1970s but were actually contemporary photos of the few remaining places in Seoul where you can see the old-style neighborhoods.  Decades of hell-bent urban development had developed (and re-developed) Seoul's neighborhoods beyond recognition, and narrow roads crowded with old-style homes (도시 한옥) were now a thing of the past--with the exception of specially designated tourist neighborhoods like Seoul's Bukcheon.  

What was left was a sense of loss and nostalgia, and rather than the "poverty porn" of similar photographs in the U.S. (and in my own city of Baltimore), these social media postings vacillated between sentimental longing for a time before Korea's "Apartment Republic," when crowded streets and narrow, connected homes meant that people knew each other in multifaceted, intimate ways, on the one hand, and the commodification of that nostalgia for capital gain, on the other.  Extremely popular programs like "Reply 1988" (응답하라 1988) were on television, steeped in nostalgia for the previous times.   It is no mistake that this television drama ends with the complete destruction of the neighborhood under a redevelopment scheme--this was the experience of an entire generation of people (the 386 generation) who came of age in the 1980s. 





(Final scenes of the old neighborhood in Reply 1988 (2015))

Similarly, Facebook and Twitter were filled with photographs and remembrances of vanishing neighborhoods, and I began to follow certain account and Facebook groups that were particularly rich sources of postings and re-postings of images of alleyways.  The first diagram shows tweets containing the keyword "alleyway" (골목길), ranging from "singleton" posts and photographs to others that were re-tweeted many times.  Photos from Ehwahdong (이화동), Bukcheongmaul (북정마을) and other neighborhoods were re-tweeted multiple times.  



On the other hand, people posting these photos located themselves in various ways that suggested a very cursory--or even incidental----identification with these neighborhoods and with place in general. The diagram below labels people's tweets about Seoul alleyways by the locations they've set in their profiles.  


While some locations (e.g., "In an alleyway") suggest a slightly cheeky identification with these places, others ("the world," "In bed") suggest a more alienated, fragmented relationship to life and sociality in Seoul.

I started to collect and archive these diverse media on "Storify", an SNS platform that allows you to aggregate and comment upon other SNS content.  Storify enabled me to keep a notebook in a digital age when the ephemerally of content could easily mean that a post to Instagram today may be very difficult to find again tomorrow.


(a screenshot of a Storify collection of social media posts about Seoul's "Jangsu Village".)

Finally, I began to take pictures of Seoul alleyways, starting with one from my old neighborhood in Gileumdong, and then posting them on Twitter.  Concerned with issues of privacy, I was careful to avoid photos of people and personal effects.  I confined myself to public streets that, while reflective of Seoul’s older development, did not suggest poverty. 



These images were re-tweeted and “liked” a few times, and were finally posted to a Facebook fan group collecting images of Korean alleyways.  The graph below shows users posting to the Facebook group, with their names replaced by the numbers of “likes” for each post.  The posts led to some online interviews about Seoul’s spaces and photography, but, by summer of 2015, much of this particular arc of photographic circulation had ceased.



Conclusions

This is not visual anthropology. And nor is this “ethnographic” in any complete sense—it’s a fragment of ethnographic work refracted onto different media, different digital platforms.  A key component of this multimodality is its social dimension.  This isn’t just me posting some mediocre photos of empty streets; my engagement with these media brings along the engagements of hundreds of other users together with the circulations of their media.  We’re linked—networked—in a way that undermines the pretensions of ethnographic author-ship (and authority).   With multimodality, we may not always get to say what we mean--or, rather, "saying" and "meaning" are the aggregate decisions of multiple nodes.  Multimodality pulls our work into different productions, different circulations and, ultimately, different meanings.  We can utilize various analytics to trace these differentiated contexts, but we must endeavor to do so in order to not just examine the way the significance of our ethnographic changes according to each configuration, but to also trace the socialities each platforms engenders. 

An Invitation

If you are also working along multimodal lines in your research, please consider submitting something to us in the "Multimodal Anthropologies" section of American Anthropologist.   

We are accepting essays (print and photo), review essays and reviews that explore the contours of multimodality for possible publication in American Anthropologist.  To submit a   manuscript, or for more information, please contact us:

Harjant Gill (hgill@towson.edu)
Matthew Durington (mdurington@towson.edu)
Samuel Collins (scollins@towson.edu)

@Dept. of Sociology and Anthropology
Towson University
8000 York Rd.
Towson, MD 21252
USA