Data science can be pretty badass, but…

Every so often I’m reminded of the power of data science. Today I attended a talk entitled ‘Spatiotemporal Crime Prediction Using GPS & Time-tagged Tweets” by Matt Gerber of the UVA PTL. The talk was a UMD CLIP event (great events! Go if you can!).

Gerber began by introducing a few of the PTL projects, which include:

  • Developing automatic detection methods for extremist recruitment in the Dark Net
  • Turning medical knowledge from large bodies of unstructured texts into medical decision support models
  • Many other cool initiatives

He then introduced the research at hand: developing predictive models for criminal activity. The control model in this case use police report data from a given period of time to map incidents onto a map of Chicago using latitude and longitude. He then superimposed a grid on the map and collapsed incidents down into a binary presence vs absence model. Each square in the grid would either have one or more crimes (1) or not have any crimes (-1). This was his training data. He built a binary classifier and then used logistic regression to compute probabilities and layered a kernel density estimator on top. He used this control model to compare with a model built from unstructured text. The unstructured text consisted of GPS tagged Twitter data (roughly 3% of tweets) from the Chicago area. He drew the same grid using longitude and latitude coordinates and tossed all of the tweets from each “neighborhood” (during the same one month training window) into the boxes. Then, using essentially a one box=one document for a document based classifier, he subjected each document to topic modeling (using LDA & MALLET). He focused on crime related words and topics to build models to compare against the control models. He found that the predictive value of both models was similar when compared against actual crime reports from days within the subsequent month.

This is a basic model. The layering can be further refined and better understood (there was some discussion about the word “turnup,” for example). Many more interesting layers can be built into it in order to improve its predictive power, including more geographic features, population densities, some temporal modeling to accommodate the periodic nature of some crimes (e.g. most robberies happen during the work week, while people are away from their homes), a better accommodation for different types of crime, and a host of potential demographic and other variables.

I would love to dig deeper into this data to gain a deeper understanding of the conversation underlying the topic models. I imagine there is quite a wealth of deeper information to be gained as well as a deeper understanding of what kind of work the models are doing. It strikes me that each assumption and calculation has a heavy social load attached to it. Each variable and each layer that is built into the model and roots out correlations may be working to reinforce certain stereotypes and anoint them with the power of massive data. Some questions need to be asked. Who has access to the internet? What type of access? How are they using the internet? Are there substantive differences between tweets with and without geotagging? What varieties of language are the tweeters using? Do classifiers take into account language variation? Are the researchers simply building a big data model around the old “bad neighborhood” notions?

Data is powerful, and the predictive power of data is fascinating. Calculations like these raise questions in new ways, remixing old assumptions into new correlations. Let’s not forget to question new methods, put them into their wider sociocultural contexts and delve qualitatively into the data behind the analyses. Data science can be incredibly powerful and interesting, but it needs a qualitative and theoretical perspective to keep it rooted. I hope to see more, deeper interdisciplinary partnerships soon, working together to build powerful, grounded, and really interesting research!

 

Rethinking Digital Democracy- More reflections from #SMSociety13

What does digital democracy mean to you?

I presented this poster: Rethinking Digital Democracy v4 at the Social Media and Society conference last weekend, and it demonstrated only one of many images of digital democracy.

Digital democracy was portrayed at this conference as:

having a voice in the local public square (Habermas)

making local leadership directly accountable to constituents

having a voice in an external public sphere via international media sources

coordinating or facilitating a large scale protest movement

the ability to generate observable political changes

political engagement and/or mobilization

a working partnership between citizenry, government and emergency responders in crisis situations

a systematic archival of government activity brought to the public eye. “Archives can shed light on the darker places of the national soul”(Wilson 2012)

One presenter had the most systematic representation of digital democracy. Regarding the recent elections in Nigeria, he summarized digital democracy this way: “social media brought socialization, mobilization, participation and legitimization to the Nigerian electoral process.”
Not surprisingly, different working definitions brought different measures. How do you know that you have achieved digital democracy? What constitutes effective or successful digital democracy? And what phenomena are worthy of study and emulation? The scope of this question and answer varies greatly among some of the examples raised during the conference, which included:

citizens in the recent Nigerian election

citizens who tweet during a natural disaster or active crisis situation

citizens who changed the international media narrative regarding the recent Kenyan elections and ICC indictment

Arab Spring actions, activities and discussions
“The power of the people of greater than the people in power” a perfect quote related to Arab revolutions on a slide from Mona Kasra

the recent Occupy movement in the US

tweets to, from and about the US congress

and many more that I wasn’t able to catch or follow…

In the end, I don’t have a suggestion for a working definition or measures, and my coverage here really only scratches the surface of the topic. But I do think that it’s helpful for people working in the area to be aware of the variety of events, people, working definitions and measures at play in wider discussions of digital democracy. Here are a few question for researchers like us to ask ourselves:

What phenomenon are we studying?

How are people acting to affect their representation or governance?

Why do we think of it as an instance of digital democracy?

Who are “the people” in this case, and who is in a position of power?

What is our working definition of digital democracy?

Under that definition, what would constitute effective or successful participation? Is this measurable, codeable or a good fit for our data?

Is this a case of internal or external influence?

And, for fun, a few interesting areas of research:

There is a clear tension between the ground-up perception of the democratic process and the degree of cohesion necessary to affect change (e.g. Occupy & the anarchist framework)

Erving Goffman’s participant framework is also further ground for research in digital democracy (author/animator/principal <– think online petition and e-mail drives, for example, and the relationship between reworded messages, perceived efficacy and the reception that the e-mails receive).

It is clear that social media helps people have a voice and connect in ways that they haven’t always been able to. But this influence has yet to take any firm shape either among researchers or among those who are practicing or interested in digital democracy.

I found this tweet particularly apt, so I’d like to end on this note:

“Direct democracy is not going to replace representative government, but supplement and extend representation” #YES #SMSociety13

— Ray MacLeod (@RayMacLeod) September 14, 2013

 

 

Reflections on Digital Dualism & Social Media Research from #SMSociety13

I am frustrated by both Digital Dualism and the fight against Digital Dualism.

Digital dualism is the belief that online and offline are different worlds. It shows up relatively harmlessly when someone calls a group of people who are on their devices “antisocial,” but it is much more harmful in the way it pervades the language we use about online communication (e.g. “real” vs. “virtual”).

Many researchers have done important work countering digital dualism. For example, at the recent Social Media & Society conference, Jeffrey Keefer briefly discussed his doctoral work in which he showed that the support that doctoral students offered each other online was both very real and very helpful. I think it’s a shame that anyone ever doubted the power of a social network during such a challenging time, and I’m happy to see that argument trounced! Wooooh, go Jeffrey! (now a well-deserved Dr Keefer!)

Digital dualism is a false distinction, but it is built in part on a distinction that is also very real and very important. Online space and offline spare are different spaces. People can act in either to achieve their goals in very real ways, but, although both are very real, they are very different. The set of qualities with which the two overlap and differ and even blur into each other changes every day. For example, “real name” branding online and GPS enabled in-person gaming across college campuses continue to blur boundaries.

But the private and segmented aspects of online communication are important as well. Sometimes criticism of online space is based on this segmentation, but communities of interest are longstanding phenomena. A book club is expected to be a club for people with a shared interest in books. A workplace is a place for people with shared professional interests. A swim team is for people who want to swim together. And none of these relationships would be confused with the longstanding close personal relationships we share with friends and family. When online activities are compared with offline ones, often people are falsely comparing interest related activities online with the longstanding close personal ties we share with friends and family. In an effort to counter this, some have take moves to make online communication more unified and holistic. But they do this at the expense of one of the greatest strengths of online communication.

Let’s discuss my recent trip to Halifax for this conference as an example.

My friends and family saw this picture:

Voila! Rethinking Digital Democracy! More of a "Hey mom, here's my poster!" shot than a "Read and engage with my argument!" shot

Voila! Rethinking Digital Democracy! More of a “Hey mom, here’s my poster!” shot than a “Read and engage with my argument!” shot

My dad saw this one:

Not bad for airport fare, eh?

Not bad for airport fare, eh?

This picture showed up on Instagram:

2013-09-16 15.27.43

It’s a glass wall, but it looks like water!

People on Spotify might have followed the music I listened to, and people on Goodreads may have followed my inflight reading.

My Twitter followers and those following the conference online saw this:

Talking about remix culture! Have I landed in heaven? #SMSociety13 #heaveninhalifax #niiice

— Casey Langer Tesfaye (@FreeRangeRsrch) September 15, 2013

And you have been presented with a different account altogether

This fractioning makes sense to me, because I wouldn’t expect any one person to share this whole set of interests. I am able to freely discuss my area of interest with others who share the same interests.

Another presenter gave an example of LGBT youth on Facebook. The lack of anonymity can make it very hard for people who want to experiment or speak freely about a taboo topic to do so without it being taken out of context. Private and anonymous spaces that used to abound online are increasingly harder to find.

In my mind this harkens back a little to the early days of social media research, when research methods were deeply tied to descriptions of platforms and online activity on them. As platforms rose and fell, this research was increasingly useless. Researchers had to move their focus to online actions without trying to route them in platform or offline activity. Is social media research being hindered in similar ways, by answering old criticisms instead of focusing on current and future potential?  Social media needs to move away from these artificial roots. Instead of countering silly claims about social media being antisocial or anything more than real communication, we should focus our research activities on the ways in which people communicate online and the situated social actions and behaviors in online situations. This means, don’t try to ferret out people from usernames, or sort out who is behind a username. Don’t try to match across platforms. Don’t demand real names.

Honestly, anyone who is subjected to social feeds that contain quite a bit of posts outside their area of interest should be grateful to refocus and move on! People of abstract Instagram should be thrilled not to have seen a bowl of seafood chowder, and my family and friends should be thrilled not to have to hear me ramble on about digital dualism or context collapse!

I would love to discuss this further. If you’ve been waiting to post a comment on this blog, this is a great time for you to jump in and join the conversation!

Reflections on Social Network Analysis & Social Media Research from #SMSociety13

A dispatch from a quantitative side of social media research!

Here are a few of my reflections from the Social Media & Society conference in Halifax and my Social Network Analysis class.

I should first mention that I was lucky in two ways.

  1. I finished the James Bond movie ‘Skyfall’ as my last Air Canada flight was landing. (Ok, I didn’t have to mention that)
  2. I finished my online course on Social Network Analysis  hours before leaving for a conference that kicked off with an excellent  talk about Networks and diffusion. And then on the second day of the conference I was able to manipulate a network visualization with my hands using a 96 inch touchscreen at the Dalhousie University Social Media Lab  (Great lab, by the way, with some very interesting and freely available tools)

 

This picture doesn't do this screen justice. This is *data heaven*

This picture doesn’t do this screen justice. This is *data heaven*

Social networks are networks built to describe human action in social media environments. They contain nodes (dots), which could represent people, usernames, objects, etc. and edges, lines joining nodes that represent some kind of relationship (friend, follower, contact, or a host of other quantitative measures). The course was a particularly great introduction to Social Network Analysis, because it included a book that was clear and interesting, a set of youtube videos and a website, all of which were built to work together. The instructor (Dr Jen Golbeck, also the author of the book and materials) has a very unique interest in SNA which gives the class an important added dimension. Her focus is on operational definitions and quantitative measures of trust, and because of this we were taught to carefully consider the role of the edges and edge weights in our networks.

Sharad Goel’s plenary at #SMSociety13 was a very different look at networks. He questioned the common notion of viral diffusion online by looking at millions of cases of diffusion. He discovered that very few diffusions actual resemble any kind of viral model. Instead, most diffusion happens on a very small scale. He used Justin Bieber as an example of diffusion. Bieber has the largest number of followers on Twitter, so when it he posts something it has a very wide reach (“the Bieber effect”). However, people don’t share content as often as we imagine. In fact, only a very small proportion of his followers share it, and only a small proportion of their followers share it. Overall, the path is wide and shallow, with less vertical layers than we had previously envisioned.

Goel’s research is an example of Big Data in action. He said that Big Data methods are important when the phenomenon you want to study happens very infrequently (e.g. one in a million), as is the case for actual instances of viral diffusion.

His conclusions were big, and this line of research is very informative and useful for anyone trying to communicate on a large scale.

Sidenote: the term ‘ego network’ came up quite a few times during the conference, but not everyone knew what an ego network is. An ego network begins with a single node and is measured by degrees. A one degree social network looks a bit like an asterisk- it simply shows all of the nodes that are directly connected to the original node. A 1.5 degree network would include the first degree connections as well as the connections between them. A two degree network contains all of the first degree connections to these nodes that were in the one degree network. And so on.

One common research strategy is to compare across ego networks.

My next post will move on from SNA to more qualitative aspects of the conference

Source: https://twitter.com/JeffreyKeefer/status/378921564281921537/photo/1 This was the backdrop for a qualitive panel

Source: https://twitter.com/JeffreyKeefer/status/378921564281921537/photo/1
This was the backdrop for a qualitative panel. It says “Every time you say ‘data driven decision’ a fairy dies.