Abstract for a paper-in-progress: quarantine and sentiment analysis.

 

 

 

A Beautiful Day in the Neighborhood: sentiment analyses of new connections and communities in a COVID world.

 

 

Quarantine re-makes the city around us, re-defining “inside” and “outside,” “home” and “neighborhood.”  “Staying home” means complying with a socially and politically constructed bubble that delimits not only who or what can move from one side or another, but the protocols to be followed when that barrier is breached.  Moreover, transitioning from one to another is not just a matter of spatial movement, it also involves a shift in identity, from the one quarantined to the one not quarantined.  Finally, quarantine is a temporal state: fourteen days, or until the city lifts the quarantine measures.  Under these conditions, what does “home” mean?  What does “inside” mean?  And when one is quarantined, what do more collective identities like “community” and “neighborhood” mean?  Under these circumstances, “home” can have a negative valence—it can be isolating and alienating from the people around you.  On the other hand, “home” can be a source of new realizations of self, and new formed of connectedness and solidarity.  In this project, I utilize a large set of Twitter data gathering thoughts on quarantine from different countries at different times, from March to September.  Mostly urban, the tweets originate in cities undergoing quarantine from around the world: Seoul, Paris, New York, each instituting different quarantine protocols at different times.  Using sentiment analysis and textual analysis, I examine Twitter as 1) a source of positive and negative valuations of quarantine; and 2) as a record of activities and relationships forged under quarantine.  On the one hand, preliminary results would seem to validate dire predictions from Durkheim, Simmel and others with regards to alienation in the city.  And, indeed, many people use Twitter to bemoan their isolation and their truncated lives.  On the other, many Twitter users explore the possibility of new connections with self and with community amidst physical separation.  In this, quarantine’s temporality plays an importance role by allowing people to construct visions of community and togetherness as a future temporality.  This paper explores the possibilities for building urban community in a pandemic world through an exploration of the way “home” and “neighborhood” have been re-conceptualized.  Ultimately, what comes from this research are insights into being together while being apart, and “home” as a staging area for the construction of community.  The essay ends with hopeful speculations on a post-pandemic city that retains communal solidarity while maintaining distancing.

#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.  

Defining anthropological community through #anthroboycott

Back on my pc--and here's my whole visualization for #AAA2015.

It's the largest set of tweets I've ever mapped from AAA: 21, 879 edges, 3543 nodes.  I ran it when I got to my office on Monday, November 23 and it covers the whole 8 day window that includes some pre- and post-tweets.  I used the Clauset-Newman-Moore cluster algorithm to group the tweets--said to be particularly effective in revealing community structures in large networks.  Finally, each identified "group" is arranged in its own box, courtesy of the Harel-Koren Fast Multiscale layout algorithm.  Nice!  That said, it's hard to beat Marc Smith, who mapped out the network on Saturday, November 21.  He's got a neater graph than mine--it's his software, after all!  But I still wanted to work through my own data.

In many ways, the graph is typical of associations.  Marc Smith et al (2014) might call this an example of a "tight crowd": "highly interconnected people with few isolated participants."  And yet, there are some definite clusters here, suggestive of what they call a "community cluster": "Some popular topics may develop multiple smaller groups, which often form around a few hubs each with its own audience, influencers, and sources of information."  Let's look at the individual clusters themselves.  Each has been identified with its own color.

Here are some of the larger "groups":

1. Dark Blue: Boycott resolution.
2. Light blue: Panels discussion.
3. Forest green: Tweets from the AAA, their re-tweets and their discussion.
4. Light green: Discussion around medical anthropology, associated panels and events.
5. Orange: The anthropology of education, associated panels and events.

On the basis of this, I would argue that the AAA conference is stuck somewhere between the "tight crowd" (typical of organizations) and the "community cluster"; in other words, the AAA conference combines homogeneous groups of people mostly concerned with their particular topics and communities with larger interests that span different clusters.

Next, I ranked the Twitter accounts by betweenness centrality, which measures the importance of a node (or vertex) based on the number of times it falls "between" two nodes on the shortest path between them.  "Importance," here, then, is different than just simply popularity; instead, betweenness centrality measures some of the importance of a node to the flow of information.

1. americananthro

2. anthroboycott

3. palestinetoday

4. benabyad

5. omanreagan

6. pacbi

7. cultanth

8. cmcgranahan

9. jasonantrosio

10. socmedanthro

Nodes with high betweenness centrality may act as "brokers" or "gateways" for flows of information and influence between different clusters.  It's worth noting that associations and group Twitter accounts (americananthro, cultanth, socmedanthro) are represented as well as the Twitter accounts of particular active individuals (omanreagan, cmcgranahan, jasonantrosio, etc.).  

But I want to concentrate on a few: anthroboycott, palestinetoday and benabyad.  These Twitter accounts have high betweenness centrality, and they serve to connect these different clusters that would, otherwise, lack even their comparatively modest connectivity.  

This is readily evident in this graph, where I filtered to include only tweets that contained the hashtag #anthroboycott.

Here are some of the top tweets (measured by the in-degree centrality of their associated node).  Much of the traffic concerned a few themes: 1) the historic vote, and the clear majority of the pro-boycotters.  2) solidarity with various pro-Palestinian groups.  3) discussions of the procedures during the boycott vote.

1. VICTORY at #aaa2015: @americananthro Clears the Way for Final Vote on #AnthroBoycott https://t.co/GSWdohHWeQ

2. RESOLUTION 2 PASSES! #Anthroboycott #BDS #AAA2015

3. Over 1500 people at #AAA2015 for the #Anthroboycott! https://t.co/by3QWTqAsp

4. RT @OmanReagan: Everyone who stays to vote on Resolution #2 can have a free drink @WennerGrenOrg party after! #Anthroboycott #AAA2015

5. #AAA2015: Congratulations to the organisers of the #Anthroboycott! https://t.co/vra6uc0BG4 https://t.co/eXAVKkI7Pe

6. I reported on2014 #Gaza war, when #Israel bombed universities. Tel Aviv U released statement gvg support for army #AnthroBoycott #AAA2015

7. E. Williams and J. Pierre discussing #anthroboycott @aba_aaa members to attend @AmericanAnthro #aaa2015 #abapanels https://t.co/SiaiQEfoQW

8. Now: motion to DIVEST from Israel. #BDS #Anthroboycott #AAA2015 https://t.co/hg1xUeXRFI

We could conclude many things from these graphs, but I want to suggest that these say something about anthropology and anthropologists in the American Anthropological Association.  Divided into subfields and sections and, generally, communicating with others in their specializations, anthropologists at the annual meeting may have little in common with others who also identify as anthropologists.  Most of our tweets are variations on live-tweeting that summarize themes we've picked out of papers and panels--in other words, tweets that are tightly coupled to our own, narrow interests and specialities.  And yet, certain issues (and their lively discussion) serve to cross these different clusters.

Are these issues, then, defining moments for anthropologists in the AAA?   If we go back to earlier AAA conferences where anthropologists were asked to take a political or ethical stand on issues (e.g., last year's #BlackLivesMatter), we can see similar patterns, with the protest against police violence spanning multiple groups.  Here's a graph from Marc Smith (again!) from last year:

From the Nodexl Graph Gallery

Would it be too much to suggest that these ethical and political orientations are what brings anthropologists together?  That it's not just a "public anthropology" (in the abstract), but a concrete politics?  It's certainly something for the AAA to contemplate--these critical moments of public anthropology are performed amidst the American Anthropological Annual Meeting, but they are not orchestrated by the AAA.  Indeed, they seem to proliferate despite (or because?) of the efforts of the AAA to quell the emergence of this kind of public anthropology in the association.  Indeed, despite predictions that the politicization of the Association will "break apart" the AAA (something I heard several times from different people in Denver), the exact opposite seems to happen.

YouTube Presentation: Social Network Analysis for Qualitative Research

I did this for the American Anthropological Association last month.

http://www.youtube.com/watch?v=9A9VDFYemg8

Anthropology on the Long Tail

Small Big Data?

Of the many hyperbolic predictions in bestselling books devoted to big data, none is more astounding than Mayer-Schönberger’s and Cukier’s claims that big data will eliminate the need for sampling (why sample when you’ve got all the data?). But here’s the thing. We don’t have all of the data. Let’s look at Twitter. First, people who tweet are not a representative sample of the population. Second, like most commercial platforms, Twitter has moved towards more proprietary policies on the data they have mined from us. Most of us can only access up to 1% of relevant tweets for a given query. That can still be a lot of tweets, and that data is, for the moment, free. But is that big data? In other words, we’ve got sampling bias. If you can detect it, though, you can correct for it—Morstatter et al recommend bootstrapping the data in order to correct for the biased sample.

But it may not be so easy with some of the work we do. For example, the authors note that the difficulties that researchers may have with the long tail of tweets—the 99 percent of hashtags that are not trending. Are these biased? And can that bias be corrected? Research so far has been on the popular terms–#Ferguson, #Obamacare. But for the most part, anthropologists study the long tail: the lives and perspectives of people engaged in quotidian action on a relatively small scale. Heck, we are the long tail: even if we engage in public anthropologies, those anthropologies (and their publics) rarely register a blip in the winner-take-all logic of power-law social media.

On the other hand, our fieldwork is rarely about achieving a certain sample size—it’s about collecting a range of experiences and practices and then contextualizing those results. With social media, we should take the same approach. We my not have big data, but we might use the same tools. And they can still be helpful, but not as a substitute for our painstaking, field research.

The following are two, quick examples of utilizing social network analysis for qualitative research drawn from a webinar I did for AAA in November (eventually to be posted on AAA’s YouTube channel). Both examples utilize a free and open source application for Microsoft Excel—NodeXL–which has the advantage of familiarity and also comes built-in queries for multiple SNS APIs: Twitter, YouTube, Flickr, and, with a little work, Facebook and hyperlink analysis. Finally, all of the complexities of graph theory are already built into the application.

In way of introduction: you’ve got nodes, dots representing people, concepts, organizations, etc., and edges, lines that represent some kind of relationship between the nodes. And although there are many ways we might analyze these relationships, for these examples I only use one measure of centrality—the relative importance of a node. Betweenness centrality ranks the importance of a node based on the number of times it’s crossed in the shortest path between all of the nodes in a graph to each other.

Who are my interlocutors?

I’ve been researching the intersection of place and social network platforms in Seoul, and one of my favorite places has been Gwanghwamun Plaza. But it’s a crowded field of social action, with events

Screenshot of NodeXL

overlapping each other every day, a complexity reflected in the tweets containing Gwanghwamun. First, I use the drop-down menu on NodeXL to query the Twitter API.

Choosing the Twitter Search Network, I enter in a search term 광화문 (Gwanghwamun) and set the parameters for my search. It returns 1528 vertices (dots) representing Twitter accounts connected by 1880 edges (lines) representing relationships between users who have  used the term, or users who were replied to or were mentioned in one of the tweets with that term. It’s pretty messy, but NodeXL gives us some options for ordering this chaos. After running metrics for the data, I have it group the nodes together into separate boxes by connected components.

Screenshot of Twitter users. Note that Twitter IDs have been cropped off

Now I’ve got something more manageable: a series of groups that share some thematic similarities. This can give me a sense of the demonstrations, counter-demonstrations, unconnected events and encounters that make up the social practice of this space. Moreover, I can rank the nodes by centrality to find the most important Twitter accounts. So now I have a sense of this field in a way that both is and is not co-extensive with the physical fieldsite, but

without obscuring the role of physical place: it does matter, after all, that the protest is happening here in Gwanghwamun and not in Second Life.

What does my event mean?

Honfest is a highly commercialized neighborhood festival concocted by a neighborhood entrepreneur in order to brand the neighborhood for commodified consumption. As such, it is a flashpoint of contention, a social drama that reveals the divisions around gentrification, race and class in this formerly working-class neighborhood in north Baltimore.

Twitter users grouped into boxes by connected component and ranked by betweenness centrality

We have sent students into the festival every year in order to document these negotiations. But we are not the only ones. Every year, there are hundreds of people posting their media on social networking sites like Instagram and Flickr. These images are important clues to the meaning of this event and analyzing these data can tell us much about the different ways people categorize space and place: photo elicitation and photovoice applied to social media platforms

Going back to the pull down import menu on NodeXL, I download the related tag network on Flickr for Honfest.

This graph shows the relationship between tagged terms, and, like the preceding example, this may prove too messy for analysis. So: we can again run metrics, and remove terms that occur less frequently in these photos.

Now, we have honfest (at the center of the graph) surrounded by a constellation of terms that co-occur with it. Like the preceding example, I can rank these terms by betweenness centrality—and we find predictable terms: hairspray (the film and the hair product), beehive (the hair style), retro etc.

Concluding thoughts

Both of these examples represent ways that we might utilize socially networked data (rather than big data) to open up our ethnographic work to other meanings, interlocutors and social relations. In neither case is the critical need for face-to-face ethnography eliminated. In fact, just the opposite. In the first example, Twitter helps us to identify issues and people that might be salient to fieldwork, while in the second, tags suggest (but only suggest) different discourses swirling around an urban festival. Both are only first steps in different phases of a sustained, ethnographic project. In other words: in the absence of big data, we still have the tools (if not the truth claims) of big data. We can utilize them to enrich our small-scale, place-bound ethnographic research in ways that are complementary.