more tweets, more votes: biden edition

The main empirical claim of the More Tweets, More Votes paper is that in the 2010 and 2012 Congressional elections, candidates who got more tweets relative to their competitor got more votes relative to the competitor. For a long time, I thought that Trump was an exception to the rule. He always gets attention, no matter if he’s winning or losing. Even in the original data, we found many cases where scandals, and other factors, could create exception to the MTMV line.

Well, this recent article in Axios suggests that maybe even Trump, the master of social media trolling, may actually be conforming to the MTMV hypothesis. They don’t use the MTMV methodology, but use a different measurement of engagement online to show that Biden now has more Twitter attention than Trump. Sociologically, what is happening is that people are realizing that Trump is probably going to lose and they’re paying attention to the expected winner. If you have other explanations of the graph, please use the comments.

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i declare complete victory in the more tweets, more votes debate

In 2013, my collaborators and I published a paper claiming that there is an empirical correlation between relative social media activity and relative vote counts in Congressional races. In other words, if people are talking about the Democrat more than the Republican on Twitter, then the Democrat tends to get more votes. Here’s the regression line from the original “More Tweets, More Votes” paper:

People grumbled and complained. But little by little, evidence came out showing that the More Tweets/More Votes model was correct. For example, an article in Social Science Quarterly showed the same results for relative Google searches and senate races:

Latest evidence? It works for wikipedia as well. Public Opinion Quarterly published a piece called “Using Wikipedia to Predict Election Outcomes: Online Behavior as a Predictor of Voting” by Benjamin Smith and Abel Gustafason. From the abstract:

We advance the literature by using data from Wikipedia pageviews along with polling data in a synthesized model based on the results of the 2008, 2010, and 2012 US Senate general elections. Results show that Wikipedia pageviews data significantly add to the ability of poll- and fundamentals-based projections to predict election results up to 28 weeks prior to Election Day, and benefit predictions most at those early points, when poll-based predictions are weakest.

Social media DOES signal American election outcomes! I spike the football. I won. Period.

It’s pretty rare that you propose a hypothesis, your prove it’s right and then it is proved right a bunch of times by later research.

#winning

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more tweets, more votes: wired magazine

A few weeks ago, Wired magazine discussed how you can use social media data to improve political forecasting. From Emma Ellis:

Traditional polling methods aren’t working the way they used to. Upstart analytics firms like Civis and conventional pollsters like PPP, Ipsos, and Pew Research Institute have all been hunting for new, more data-centric ways to uncover the will of the whole public, rather than just the tiny slice willing to answer a random call on their landline. The trending solution is to incorporate data mined from the Internet, especially from social media. It’s a crucial, overdue shift. Even though the Internet is a cesspool of trolls, it’s also where millions of Americans go to express opinions that pollsters might not even think to ask about.

And they were kind enough to cite the More Tweets/More Votes research:

According to Fabio Rojas, a sociologist at Indiana University who conducted a study correlating Twitter mentions and candidate success, “More tweets equals more votes.”…

Social media data gives you a sense of the zeitgeist in a way that multiple choice questions never will. “Say I wanted to learn about what music people are listening to,” says Rojas. “I would have to sit down beforehand and come up with the list. But what if I don’t know about Taylor Swift or Justin Bieber?” Polls are generated by a small group of people, and they can’t know everything. Social media is a sample of what people actually talk about, what actually draws their attention, and the issues that really matter to them.

That sentiment matters, and pollsters can (and in PPP’s case, do) use it to direct their questioning. “People clue us in on stuff online all the time,” says Jim Williams, a polling analyst at PPP. They even ask the Internet where and on what they should poll next, hence Harambe’s presence in its poll. But, Williams says, joke suggestions aside, Twitter’s input also helps pollsters include the finer points of local and national politics. And even the Harambe question itself actually tells the pollsters something interesting.

Interesting stuff.

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more tweets, more votes: social media and causation

This week, the group Political Bots wrote the following tweet and cited More Tweets, More Votes in support:

why do politicians buy bots? "statistically significant association" more tweets more votes U.S. House by @fabiorojas http://t.co/gS6FjEN9Ig

— politicalbots.org (@polbots) September 2, 2015

The claim, I believe, is that politicians purchase bots (automated spamming Twitter accounts) because they believe that more presence on media leads to a higher vote tally.

In presenting these results, we were very careful to avoid saying that there is a causal relationship between social media mentions and voting:

These results indicate that the “buzz” or public discussion about a candidate on social media can be used as an indicator of voter behavior.

And:

Known as the Pollyana hypothesis, this finding implies that the relative over-representation of a word within a corpus of text may indicate that it signifies something that is viewed in a relatively positive manner. Another possible explanation might be that strong candidates attract more attention from both supporters and opponents. Specifically, individuals may be more likely to attack or discuss disliked candidates who are perceived as being strong or as having a high likelihood of winning.

In other words, we went to great efforts to suggest that social media is a “thermometer,” not a cause of election outcomes.

Now, it might be fascinating to find that politicians are changing behavior in response to our paper. It *might* be the case that when politicians believe in a causal effect, they increase spending on social media. Even then, it doesn’t show a causal effect of social media. It is actually more evidence for the “thermometer” theory. Politicians who have money to spend on social media campaigns are strong candidates and strong candidates tend to get more votes. I appreciate the discussion of social media and election outcomes, but so far, I think the evidence is that there is not a casual effect.

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dear UK: more tweets, more votes!!!!

Previous More Tweets, More Votes coverage

The Oxford Internet Institute reports that Twitter data picked up some of the trends in last week’s election, when traditional polling did poorly. In their blog, they ask – did social media suggest the massive upset from last week? Answer, somewhat:

The data we produced last night produces a mixed picture. We were able to show that the Liberal Democrats were much weaker than the Tories and Labour on Twitter, whilst the SNP were much stronger; we also showed more Wikipedia interest for the Tories than Labour, both things which chime with the overall results. But a simple summing of mention counts per constituency produces a highly inaccurate picture, to say the least (reproduced below): generally understating large parties and overstating small ones. And it’s certainly striking that the clearly greater levels of effort Labour were putting into Twitter did not translate into electoral success: a warning for campaigns which focus solely on the “online” element.

One of the strengths of our original paper on voting and tweets is that we don’t simply look at aggregate social media and votes. That doesn’t work very well. Instead, what works is relative attention. So I would suggest that the Oxford Institute look at one-on-one contests between parties in specific areas and then measure relative attention. In the US, the problem is solved because each Congressional district has a clearly identified GOP and Democratic nominee. The theory is that when you are winning people talk about you more, even the haters. People ignore losers. Thus, the prediction is that relative social media attention is a signal of electoral strength. I would also note that social media is a noisy predictor of electoral strength. In our data, the “Twitter signal” varied wildly in its accuracy. The correlation was definitely there, but some cases were really far off and we discuss why in the paper.

Finally, I have not seen any empirical evidence that online presence is a particularly good tool for political mobilization. Even the Fowler paper in Nature showed that Facebook based recruitment was paltry. So I am not surprised that online outreach failed for Labour.

Bottom: The Oxford Internet Institute should give us a call, we can help you sort it out!

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more tweets, more votes: it works for facebook likes

When I started working on the politics/social media correlation, a few researchers told me that is doesn’t work with Facebook data. Here’s a new article argues the opposites – Facebook likes are good predictors in recent Indian elections:

Can the count of ‘likes’ recorded on the Facebook page of a party predict whether it will win the elections or not? To answer this question in the Indian setting in the context of the 2014 Lok Sabha elections, the political trend on Facebook was estimated using the numbers of ‘likes’ recorded on verified Facebook fan pages during the period of study—24 January to 12 May 2014. A strong positive correlation was found to exist between the number of ‘likes’ a party or its leader secured on their official Facebook fan page and their popular vote share. A single latent variable—presumably, the political preferences of the people—explained 91.37% of the total variance in those two variables. Furthermore, the time period during which the ‘likes’ were recorded was found to have a moderating effect on the positive relationship between the ‘likes’ and votes. It was found that the month preceding the voting period was the best to predict the vote share using ‘likes’—with 86.6% accuracy.

Check it out.

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more tweets, more votes: it works for TV!!!

Within informatics, there is a healthy body of research showing how social media data can be used for forecasting future consumption. The latest is from a study by Nielsen, which shows some preliminary evidence that Twitter activity forecasts television program popularity. In their model, adding Twitter data increases the explained variance in how well a TV show will in addition to data on promotions and network type. Here’s the summary from Adweek.

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more tweets, more votes: it works in india

A recent article in the Atlantic provides some evidence that the tweets/votes correlation holds up in the recent Indian election:

The direct comparison between volumes of tweets mentioning the different parties shows a similar movement: from a somewhat even distribution—particularly in the mid phases of the campaign between January 28 and March 3, before Kejriwal started his road show in Gujarat and his live Facebook talk—but the BJP took over in the final stages of the campaign.

They should do relative tweet measures, which helps with American data.

For previous More Tweets, More Votes – click here.

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more tweets, more votes – it works for google searches, too!

The phenomena reported in the original More Tweets/More Votes paper keeps getting replicated. Social Science Quarterly has a new paper that  replicates the MTMV model by regressing Senate election vote shares on Google search traffic. The paper is called “Google Insights and U.S. Senate Elections: Does Search Traffic Provide a Valid Measure of Public Attention to Political Candidates?“, written by C. Douglas Swearingen and Joseph T. Ripberger. Here is the key scatter plot:

Looking forward to more of this research.

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more tweets, more votes: in foreign policy, PLoS One, and hitting the top 10 list

More Tweets, More Votes news:

1. I thank Alex Hanna for mentioning this work in a new Foreign Policy piece that discusses how social media can be used to monitor elections in nations where polling is rare, a possibility that I mentioned in my Washington Post article on MTMV. Alex and co-author Kevin Harris use social media data to track Iranian public opinion, because quality polling is not common there. A must read for people who want to see how social media can be used to measure and evaluate democratic processes.
2. The peer reviewed version of MTMV is now out in PLoS One. The paper presents the tweet share/vote share correlation for the 2010 and 2012 House elections and discusses possible mechanisms.
3. The working paper version of MTMV at Social Science Research Network has had over 1,200 downloads in its short life, pushing it into the top 10 most downloaded papers on models of elections and political processes at SSRN. Congratulations to my co-authors Joe DiGrazia, Karissa McKelvey, and Johan Bollen. Outstanding work.

Insider tip: New results be presented at the computational social science workshop at the University of Chicago in January 2014. Details forthcoming.

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more tweets, more votes – media summary

If you are interested in reading the media coverage of More Tweets, More Votes, here are the links to selected coverage:

• My op-ed in the Washington Post
  
• the Wall Street Journal
• NPR
• The Daily Rundown/MSNBC
• C-SPAN’s Washington Journal
• National Journal
• The Atlantic

Thanks for checking in.

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more tweets, more votes in new york

On Monday morning, my co-authors and I will present the more tweets, more votes paper at the meeting of the American Sociological Association in New York City. Please come by. It will be at 8:30 am at the Hilton. Here is the URL for an early draft of the paper. Thanks.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2235423

more tweets, more votes – but why?

In the More Tweets, More Votes paper, we established that Twitter share correlates with future Congressional election results (e.g., % of tweets that mention GOP in a district correlates with the GOP vote share in the district). The deeper question – why? We’ve got a working paper that suggests an answer: Twitter, in some respects, mimics conventional text, which means that is close enough to the grass roots. In other words, people are more likely to use technology if it resembles what they know – an idea going back to a classic paper by Kwon and Zmud.

We can tease out testable implications. Specifically, technologies that are more sophisticated will be less likely to correlate with mass politics. In others, social media that is easy to use and relies mainly on pre-existing language skills are more likely to correlate with social trends than social media that require higher levels of functionality.

We test this with our tweets/votes data. We measured three types of candidate tweet share – “free text,” @mentions, and #hashtags. Free text is the “people’s” method of tweeting, while @mentions and #hashtags are syntaxes that require more knowledge. The grassroots hypothesis implies free text mentions of candidates will have a stronger correlation with election outcomes than @mentions or #hashtags. The results? Free texts correlate (as per the original paper) but the others are not significantly different from zero. The picture says it all.

Stark result. The implication is profound for social scientific studies of social media. If your data requires distinctly Internet based skills, it is less likely to speak to population level trends. Sophistication is probably the mark of connoisseur. Indeed, additional analysis of our data shows that @mention and #hashtag users are “intense” Internet users. For example, they have bigger median followers and are more likely to be “verified” by Twitter.

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more tweets, more votes – cause or correlation?

A number of people have asked me a very important question about the More Tweets, More Votes paper. Do relative tweet rates merely correlate with elections or is there is a causal link?

The paper itself does not settle the issue. The purpose of the paper is merely to document this striking correlation. Given that qualification, let me explain the argument from both sides and my priors.

• Correlation: Twitter is a passive record of how excited people are. If a candidate somehow garners the attention of the public, they get excited and start talking about it, which translates into a higher twitter presence.
• Causal: The unusual attention that a candidate attracts in social media sways undecided or weakly committed voters. In a sense, highly active twitter users are the “opinion leaders” of modern society.

My prior: 75% correlation, 25% cause. How would tease out these arguments? For example, what variable could instrument the district level tweet counts? Interesting to find out.

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more tweets, more votes: social media as a quantitative indicator of political behavior

Unit of analysis: US House elections in 2010 and 2012. X-Axis: (# of tweets mentioning the GOP candidate)/(# of tweets mentioning either major party candidate). Y-axis: GOP margin of victory.

I have a new working paper with Joe DiGrazia*, Karissa McKelvey and Johan Bollen asking if social media data actually forecasts offline behavior. The abstract:

Is social media a valid indicator of political behavior? We answer this question using a random sample of 537,231,508 tweets from August 1 to November 1, 2010 and data from 406 competitive U.S. congressional elections provided by the Federal Election Commission. Our results show that the percentage of Republican-candidate name mentions correlates with the Republican vote margin in the subsequent election. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district’s racial and gender composition. With over 500 million active users in 2012, Twitter now represents a new frontier for the study of human behavior. This research provides a framework for incorporating this emerging medium into the computational social science toolkit.

The working paper (short!) is here. I’d appreciate your comments.

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* Yes, he’ll be in the market in the Fall.

more (angry) tweets, more heart attacks

PS Magazine reports on research that links tweet sentiment and health:

Measuring such things is tough, but newly published research reports telling indicators can be found in bursts of 140 characters or less. Examining data on a county-by-county basis, it finds a strong connection between two seemingly disparate factors: deaths caused by the narrowing and hardening of coronary arteries and the language residents use on their Twitter accounts

…

“Given that the typical Twitter user is younger (median age 31) than the typical person at risk for atherosclerotic heart disease, it is not obvious why Twitter language should track heart disease mortality,” writes a research team led by Johannes Eichstaedt and Hansen Andrew Schwartz of the University of Pennsylvania. “The people tweeting are not the people dying. However, the tweets of younger adults may disclose characteristics of their community, reflecting a shared economic, physical, and psychological environment.”

Not a puzzle to me. I have argued that social media content is often an indicator – a smoke signal – of other trends. Thus, if people are stressed due to environmental conditions (the economy, unemployment), they will have heart attacks and write angry text. The only question is when the correlation holds. For more discussion of the more tweets/more votes/more anything phenomena, click here.

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university of chicago visit – everything you wanted to know about tweets and votes, but were afraid to ask

I will be a guest of the computational social science workshop at the University of Chicago this coming Friday. I will present a very detailed talk on the more tweets/more votes phenomena called “Everything You Wanted to Know About the Tweets-Votes Correlation, but Were Afraid to Ask.” If you want to chat or hang out, please email me.

Refreshments will be served.

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more tweets, more vote – q&a and erratum

This week, there has been substantial media coverage of the More Tweets, More Votes paper, which was presented on Monday at the ASA meeting in New York. Scholars and campaign professionals have been asking questions about the draft of the paper, which can be found here. Since we have received many requests and clarifications, I will address comments through this blog post.

1. Your tweets/votes R-squared is small. The correlation between tweets and votes is actually really small when compared with other factors (such as incumbency).

Commenters have asked about the size of the twitter correlation in comparison with other models. First, no claim was made about this issue and it not relevant to the major point of the paper. The point of the paper is that social media has important information. This information may be correlated with other data. However, we can compare the twitter bivariate correlation with other correlations. The twitter correlation with Republican vote margin, for example, is .53. Incumbency has a correlation of .73 with vote margin. The proportion of people with a college education has a correlation of .15.  Thus, the twitter measure is in the middle of the range of the variables we look at.

2.  404 out of 406?: In your SSRN draft, the analysis does not predict the winner in 404 out of 406 competitive races, which is what Fabio Rojas said in the WaPo op-ed. (http://www.washingtonpost.com/opinions/how-twitter-can-predict-an-election/2013/08/11/35ef885a-0108-11e3-96a8-d3b921c0924a_story.html?wpisrc=emailtoafriend)

A number of commenters have asked about the number of correctly predicted races. In the original paper, we do not perform this analysis. For the purposes of presenting the research to the public, we computed the rate of correct predictions (within the data), which was about 92.5%. I then multiplied this by all races (435). Therefore, the extrapolated number of correctly predicted races is 404 out of 435. If we use only the contested race subsample, we get 375 races out of 406 contested races. This is a correction of what I wrote in the op-ed, which accidentally combined these two estimates. The op-ed now contains the correction.

3. You don’t predict an election. “[…] just in case someone is paying attention: You, Have, To, Predict, In, Advance. If you don’t want to follow my advice follow that of Lewis-Beck (2005):”the forecast must be made before the event. The farther in advance […] the better”. Gayo-Avello (http://di002.edv.uniovi.es/~dani/PFCblog/)

Professor Gayo-Avello and other commenters have raised the issue of prediction. He is correct in that we didn’t use contemporary data to predict elections in the future. Rather, we use “predict” in the statistical sense. We use social media data to estimate a dependent variable within the sample.

4. The Pollyanna effect  is unsubstantiated. There is no support to say negative tweets are a good thing for a candidate.

The Pollyana effect is merely a hypothesized explanation for what we find. It requires further research and study. We make no claim that it has been established.

5. Twitter user base is not representative of the population, self-selection bias, spam, propaganda, lack of geolocation of tweets.

A number of commenters have focused on the fact that we know little about the people who write tweets, nor do we estimate whether tweets are positive or negative. This is true, but the point of the paper is not to make an estimate of who people are, or to interpret what they say. Rather, it is simply to show that that social media contains informative signals of what people might do. Remarkably, the data shows a correlation even though Twitter users are not a random sample of the population. We are simply measuring the relative attention given to a political candidate.

6. Vote share is a more natural way than vote margin to analyze and present the results, as well as consistent with prior Political Science research. (http://themonkeycage.org/2013/04/24/the-tweets-votes-curve/)

Some readers noted that traditional political science uses vote share rather than vote margin. Our updated paper corrects that. The original paper is a non-peer reviewed draft. It is in the process of being corrected, updated, and revised for publication. Many of these criticisms have already been incorporated into the current draft of the paper, which will be published within the next few months.

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more tweets & open asa slots

Want to see Big Data in action? More Tweets/More Votes will be presented on Monday, 8:30 am in the session on voting and elections.

Also, if anyone wants to chat, I can do Monday breakfast, 10:30 – 1pm-ish. I will also attend the book release party for guest blogger emeritus Hilary Levy Friedman on Monday. Her book, Playing to Win, will soon be released by the University of California Press. Email me if you want to meet up.

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no sentiment needed – comment on the tweets-vote curve

When people read our More Tweets, More Votes paper, they often wonder – where is the “sentiment analysis?” In other words, why don’t we try to measure whether a tweet is positive or negative? Joe DiGrazia, the lead author, addressed this in a recent interview with techpresident.com:

DiGrazia said the researchers were “kind of surprised” that they saw a correlation without doing sentiment analysis of the Tweets. “We thought we were going to have to look at the sentiment,” he said. He speculated that one reason for the correlation could be a so-called Pollyanna Hypothesis, “that people are more likely to gravitate toward subjects that they are positive about and are more likely to talk about candidates that they support.”

The idea is simply this: the frequency of speech is often a relatively decent approximation of how imporant people think that topic is relative to salient alternatives. If people say “Obama” a little more often than the competition, then it’s not unreasonable to believe that he is more favored. And you don’t need content analysis to suss that out.

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social media and social science: a chat with garret m. peterson

I was recently interviewed by the Economics Detective Podcast. Garret and I spoke about some general issues when you use social media data in research. Then we focused on three papers:

• More Tweets, More Votes (PLoS One 2013, w/Joe DiGrazia, Karissa McKelvey, and Johan Bollen)
• Black Lives Matter on Social Media (Ethnic and Racial Studies 2017, w/Jelani Ince and Brandon Finlay)
• Twitter’s Glass Ceiling (ICWSM 2016 w/Anne Groggel, Shirin Nilizadeh, Peter Lista, Apu Kapadia, YY Ahn and Sjrita Das).

If you like big data and social science, check it out.

++++++++

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why i will continue to be annoying at job talks

A couple of days ago, I wrote a blog post about why I think that one should be tough on job candidates during job talks. My argument boils down to a simple point – it’s my chance to push a little and see how they respond in a tough spot.

At first, I was going going to write a blog post defending this view, but then Pamela Oliver retweeted the following, which makes my point very clear:

I taught on race in my public policy course today (130+ students). One student asked what I thought about the Bell Curve & arguments RE: the cognitive inferiority of black people. The student who asked this is nice, engaged and (I believe) genuinely curious. 1/n

— Jamila Michener (@povertyscholar) May 7, 2018

Bingo. This is exactly right. In your job as a professor, you will be put under pressure. You will be asked uncomfortable questions. They will not care about  your feelings or how it conflicts with your sense of egalitarianism. If you read through Professor Michener’s thread, you will see that she handled it in a very thoughtful and professional way. The thread raises many good points, but the starting point is this: this job has moments of pressure and you need to be able to handle it well.

Just to give you a sense of how the “tough Q&A” might be helpful in assessing a person, here are examples of where “thinking on your feet” and “dealing with pressure” made a difference in my own life:

1. Around 2000, an audience member at an ASA round table said my work was offensive to all LGBT people. She then stood up and stormed out.
2. Around 2008, an audience member at an ASA panel stood up and said that my work was completely wrong. He was referring to a draft of this paper.
3. During my midterm review, the current chair indicated that I may be in trouble. It’s ok. I pulled through – we’re still friends!!!
4. My work on the More Tweets, More Votes paper was openly criticized by leading political professionals, including this Huffington Post piece.
5. I have argued with people in public about open borders. Including the spokesman of the Hungarian national government, Zoltan Kovacs. Let’s just say he doesn’t share my opinion!
6. Students will raise potentially inflammatory questions in the middle class. Last year, for example, a student claimed in class that Catholicism is the only true religion. Needed to be real careful about that one.
7. The blog generates a surprising amount of hate mail – from other scholars!
8. As a journal editor, people question my rejection letters all the time. Oddly, they never question my acceptance letters!
9. And of course, the piles and piles of journal and book editor rejections that every professor must deal with.

Of course, the typical day is not that stressful, but scholars are often called to defend themselves and they must do so in the face of tough opposition. I don’t advocate a lack of courtesy or civility. But asking about things like research design, relation to research done by scholars in adjacent fields, and inference is totally acceptable and there is nothing wrong with a courteous, but blunt, question. Heck, IU grads have told me that my questions during practice job talks were excellent prep for job talks elsewhere. Thus, if you have had years to work on a dissertation and you can’t answer a mildly assertive question about your own work, I will not be impressed.

+++++++

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The rise of Black Studies:  From Black Power to Black Studies 
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road trip – fall 2014 meet ups!!

Orgheads, I will be travelling a bit in late October and early November. If you want to hang and talk sociology, organizations, or whatever, just drop by! We’ll make some time:

• October 17: Mississippi State University – “More Tweets, More Votes.” New results + a Grad Skool Rulz bonus round.
• October 24: The University of Southern California – “The Four Histories of Black Power: A Sociological Challenge to Black Power Historical Scholarship.” ’nuff said.
• November 10-13: SocInfo 2014!! The conference that bridges computer  science and social science. This conference will be held at Yahoo Headquarters in Barcelona.

See you then!

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dial F for fabio

Need a workshop speaker? I’m here to help out! I work for free if it’s local, and I work cheap if you pitch in for travel costs. Topics:

• Black Power/Black Studies – Student protest and the rise of ethnic studies
• The Antiwar movement after 9/11 – How did the peace movement fight war in the Bush and Obama eras?
• More Tweets/More Votes – how to use social media to study politics!
• Organizational behavior and infection control – new research on how organizational behavior plays a role in patient safety
• Grad Skool pep talk!

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fabio works for tips

Desperate for a workshop speaker? Send me an email. My topics:

• The politics of the antiwar movement after 9/11
• Black Power/Black Studies
• More tweets, more votes: social media as a measurement of public opinion
• Knowledge and practice in infection control – new project on the organizational behavior of hospitals

I’ll do it for free if I can drive there. If you pick up transportation costs, I come cheap. If anyone in NYC wants me to visit in Mar/April/May, I will work for tips.

PS. I have two topics for grad student groups: grad skool rulz and public sociology. Undergrads may enjoy a discussion of my manuscript in progress on social theory.

The True Word: From Black Power/Grad Skool Rulz

data science is engineering – a guest post by karissa mckelvey

This is a guest post by Karissa McKelvey. She is affiliated with the Complex Systems PhD program at Indiana University’s School of Informatics. She works on the intersection of social media and political mobilization and has co-authored papers on Occupy Wall Street and the More Tweets/More Votes phenomenon.

Why Data Science is just a fad, and the future of the academy

We expect students to write research papers as well as do statistics in R or STATA or Matlab on small datasets. Why don’t we expect them to deal with very very large datasets? We are told that “Data Science” is the answer to this “Big Data” problem.

I’d like to redefine Data Science: it is the act of gluing toolkits together to create a pipeline from raw data to information to knowledge.There are no innovations to be made in Data Science. The innovations to be made here are in Computer Science, Informatics, Statistics, Sociology, Visualization, Math, etc. — and they always will be.

Data Science is just engineering.