"Predicting X from Twitter is a popular fad within the Twitter research subculture. It seems both appealing and relatively easy. Among such kind of studies, electoral prediction is maybe the most attractive, and at this moment there is a growing body of literature on such a topic. This is not only an interesting research problem but, above all, it is extremely difficult. However, most of the authors seem to be more interested in claiming positive results than in providing sound and reproducible methods. It is also especially worrisome that many recent papers seem to only acknowledge those studies supporting the idea of Twitter predicting elections, instead of conducting a balanced literature review showing both sides of the matter. After reading many of such papers I have decided to write such a survey myself. Hence, in this paper, every study relevant to the matter of electoral prediction using social media is commented. From this review it can be concluded that the predictive power of Twitter regarding elections has been greatly exaggerated, and that hard research problems still lie ahead."

"Social media comprises interactive applications and platforms for creating, sharing and exchange of user-generated contents. The past ten years have brought huge growth in social media, especially online social networking services, and it is changing our ways to organize and communicate. It aggregates opinions and feelings of diverse groups of people at low cost. Mining the attributes and contents of social media gives us an opportunity to discover social structure characteristics, analyze action patterns qualitatively and quantitatively, and sometimes the ability to predict future human related events. In this paper, we firstly discuss the realms which can be predicted with current social media, then overview available predictors and techniques of prediction, and finally discuss challenges and possible future directions."

And here I was worried from the headline that I might have to call out Uncle Paul.

Why isn't the appropriate regulatory model that of phone companies, which also have network externalities (rather than production economies of scale), but are required to act as common (and mere) carriers?

"Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends—except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes."

It will be interested to see how they argue this isn't confounded six ways from Sunday.

"Information popularity and social relationships are intimately connected. However, measuring the extent to which they affect each other has remained an open question. Because we now have access to rich and large data sets from online social networks, we can begin to quantitatively understand the interplay between them. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks - who is friends with whom - and the set structure of topical affiliations - who talks about what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our study is Twitter, where we look at the social network of both follower relationships and communication relationships, alongside the affiliations outlined by the hashtags used by people to label their communications. On Twitter, we demonstrate how the hashtags that a user adopts can be used to predict their social relationships, and also how the social relationships between the adopters of a hashtag can be used to predict the future popularity of that hashtag. Importantly, we find that both relationships are driven by highly computationally simple structural determinants. While our analysis focuses on Twitter, we view our analysis of social-topical affiliations as broadly applicable to a host of diverse affiliations, including the movies people watch, the brands people like, or the locations people frequent."

"There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness."

"We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the "six degrees of separation" phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which "your friends have more friends than you". Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics."

"Although the phrase "Twitter revolution" was coined back in 2009 to refer to the mass mobilizations in Moldova and soon after in Iran, year 2011 has confirmed the connection between social media and social unrest. Undoubtedly, the "Arab spring" or the "Spanish revolution" --which has spread throughout and culminated with Liberty Square occupation in New York-- cannot be understood without the role of social networking sites to help protesters self-organize and attain a critical mass of participants. In this context, we need to distinguish dynamic activity (which comprises actual information exchange) from the underlying structure (which reflects relatively stable relationships between users --who follows who). We provide a quantitative analysis which stems from complex network theory to scrutinize the mobilization's temporal evolution and its resulting structure and dynamics both at the macro- and micro-scale levels. Most importantly, we study the interplay between the structural and dynamic levels to decipher how the former facilitates the latter's success, understood as efficiency in information spreading. We discuss who (and why) has privileged spreading capabilities when it comes to information diffusion, based on the analysis of empirical data."

"Social web users are a very diverse group with varying interests, levels of expertise, enthusiasm, and expressiveness. As a result, the quality of content and annotations they create to organize content is also highly variable. While several approaches have been proposed to mine social annotations, for example, to learn folksonomies that reflect how people relate narrower concepts to broader ones, these methods treat all users and the annotations they create uniformly. We propose a framework to automatically identify experts, i.e., knowledgeable users who create high quality annotations, and use their knowledge to guide folksonomy learning. We evaluate the approach on a large body of social annotations extracted from the photosharing site Flickr. We show that using expert knowledge leads to more detailed and accurate folksonomies. Moreover, we show that including annotations from non-expert, or novice, users leads to more comprehensive folksonomies than experts' knowledge alone."

"Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes based on the information-theoretic notion of transfer entropy, or information transfer. This theoretically grounded measure is based on dynamic information, captures fine-grain notions of influence, and admits a natural, predictive interpretation. Causal networks inferred by transfer entropy can differ significantly from static friendship networks because most friendship links are not useful for predicting future dynamics. We demonstrate through analysis of synthetic and real-world data that transfer entropy reveals meaningful hidden network structures. In addition to altering our notion of who is influential, transfer entropy allows us to differentiate between weak influence over large groups and strong influence over small groups."

"I learned a lot about fandom couple of years ago in conversations with my friend Britta, who was working at the time as community manager for Delicious. She taught me that fans were among the heaviest users of the bookmarking site, and had constructed an edifice of incredibly elaborate tagging conventions, plugins, and scripts to organize their output along a bewildering number of dimensions. If you wanted to read a 3000 word fic where Picard forces Gandalf into sexual bondage, and it seems unconsensual but secretly both want it, and it's R-explicit but not NC-17 explicit, all you had to do was search along the appropriate combination of tags (and if you couldn't find it, someone would probably write it for you). By 2008 a whole suite of theoretical ideas about folksonomy, crowdsourcing, faceted infomation retrieval, collaborative editing and emergent ontology had been implemented by a bunch of friendly people so that they could read about Kirk drilling Spock." --- See also the very last link.

"While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. In this article, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homopholy both amongst individuals with similar propensities to adopt a technology and geographic location are critical to reproduce features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves."

"The paper examines the influence of geographic distance, national boundaries, language, and frequency of air travel on the formation of social ties on Twitter, a popular micro-blogging website. Based on a large sample of publicly available Twitter data, our study shows that a substantial share of ties lies within the same metropolitan region, and that between regional clusters, distance, national borders and language differences all predict Twitter ties. We find that the frequency of airline flights between the two parties is the best predictor of Twitter ties. This highlights the importance of looking at pre-existing ties between places and people." --- Not surprising, but I guess good to have confirmed.

I am deeply suspicious, from the abstract.

Disappointing.  The content variables are all completely ad hoc (the structure variables are also ad hoc, but traditional), so we really have no idea of what is being found here.  And there is no assessment of uncertainty at all.  And, for the love of Gauss, stop using R^2 like that!

"...Twitter's distinction between different types o interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links of groups (the weakness of strong ties), events transmitting information pass preferentially through links connecting different groups or even more through users acting as bridges between groups (the strength of weak ties)."

All true; also why I don't have comments.

To put it a bit more kindly than Tom does, the _cognitive_ value of traditions like Turkle's is "heuristic" in the older sense: they _make up_ speculations and conjectures, but do not combine them with data in a way that has any real force as evidence.  In the case of psychoanalysis, the track record even as heuristic is not exactly encouraging...

For next week at Stanford

Chairman Bruce annotates a lamentation (which I agree with!) over the fate of delicious, in his usual style.  (And wow, when did the Wired website get so aggressive?  His blog is much more enjoyably read via RSS.)

WANT

Why _don't_ libraries have a "people who checked out this book also checked out..." feature? (Also, I suspect other peoples' tags become most useful when employed as features in a recommendation system, rather than directly searching on them, but that's a mere guess.)

"(Attention Conservation Notice: this is an unholy mashup between historical speculation and experience from 23 Things, exacerbated by too much checking footnotes and not enough sleep)."

" focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. ... suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this ... on the Last.fm data set ... social network constructed from semantic similarity captures actual friendship [better] than Last.fm's suggestions based on listening patterns"

"Everyone who ever dealt with a Troll knows of the strong, nagging urge to argue back at him ; and they know, of course, that this urge must be repressed at all cost, for it is what Trolls feed on. Thus trolling is powered by the same basic motivation that it serves to satisfy : that crazy desire to get the last word in a conversation. Trolls exist because there is enough Trollhood in everyone of us for them to feed on." Plus: Socrates and Hui Shi as trolls.

This point came up at the NIPS networks workshop ("why don't we just engineer better political movements from our online social networks?"), and I wish I'd had the wit to respond like this.

I'm there.

CMU, October 19. Free but registration req'd.

I like the fact that the largest single element is the one reminding me to transfer things to the notebooks...

I think Steve misses a possible (and valuable) role for professional journalists: as interface specialists between various specialized sub-communities and the broader public; ones, moreover, who are not agents of their subjects (unlike PR flacks).

Special issue of _Public Choice_ on blogging and politics

"Where are the programming languages that have Bayesian filters, PageRank, and other types of collective intelligence as a central, core part of the language? I don’t mean libraries or plugines, I mean integrated into the core of the language in the sam

Bookmarking this here seems nicely recursive...

My work gets noticed in the old country!

"The problem of actually changing the world for the better is not going to be finessed with clever "online argumentation" software. To pull off that trick you have to get your hands dirty capturing, and wielding, political power."