Co-Following on Twitter

We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. We start with a machine learning problem of predicting a user's preference among two alternative choices of Twitter friends. We show that co-following information provides strong signals for diverse classification tasks and that these signals persist even when (i) the most discriminative features are removed and (ii) only relatively "sparse" users with fewer than 152 but more than 43 Twitter friends are considered. Going beyond mere classification performance optimization, we present applications of our methodology to Computational Social Science. Here we confirm stereotypes such as that the country singer Kenny Chesney (@kennychesney) is more popular among @GOP followers, whereas Lady Gaga (@ladygaga) enjoys more support from @TheDemocrats followers. In the domain of marketing we give evidence that celebrity endorsement is reflected in co-following and we demonstrate how our methodology can be used to reveal the audience similarities between Apple and Puma and, less obviously, between Nike and Coca-Cola. Concerning a user's popularity we find a statistically significant connection between having a more "average" followership and having more followers than direct rivals. Interestingly, a \emph{larger} audience also seems to be linked to a \emph{less diverse} audience in terms of their co-following.Comment: full version of a short paper at Hypertext 201

A Long-Term Analysis of Polarization on Twitter

Social media has played an important role in shaping political discourse over the last decade. At the same time, it is often perceived to have increased political polarization, thanks to the scale of discussions and their public nature. In this paper, we try to answer the question of whether political polarization in the US on Twitter has increased over the last eight years. We analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their (i) network — how people follow political and media accounts, (ii) tweeting behavior — whether they retweet content from both sides, and (iii) content — how partisan the hashtags they use are. Our analysis shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10% - 20%. Our study is one of very few with such a long-term perspective, encompassing two US presidential elections and two mid-term elections, providing a rare longitudinal analysis

Visualizing User-Defined, Discriminative Geo-Temporal Twitter Activity

We present a system that visualizes geo-temporal Twitter activity. The distinguishing features our system offers include, (i) a large degree of user freedom in specifying the subset of data to visualize and (ii) a focus on *discriminative* patterns rather than high volume patterns. Tweets with precise GPS co-ordinates are assigned to geographical cells and grouped by (i) tweet language, (ii) tweet topic, (iii) day of week, and (iv) time of day. The spatial resolutions of the cells is determined in a data-driven manner using quad-trees and recursive splitting. The user can then choose to see data for, say, English tweets on weekend evenings for the topic "party". This system has been implemented for 1.8 million geo-tagged tweets from Qatar (http://qtr.qcri.org/) and for 4.8 million geo-tagged tweets from New York City (http://nyc.qcri.org/) and can be easily extended to other cities/countries

Using Co-Following for Personalized Out-of-Context Twitter Friend Recommendation

We present two demos that give personalized `"out-of-context" recommendations of Twitter users to follow. By out-of-context we mean that a user wants to receive recommendation on, say, musicians to follow even though the user's tweets' contents and social links have no connection to the "context" of music. In this setting, where a user has never expressed interest in the context of music, many existing methods fail. Our approach exploits co-following information and hidden correlations where, say, a user's political preference might actually provide clues about their likely music preference. We implement this framework in two very distinct settings: one for recommending musicians and one for recommending political parties in Tunisia. Our framework is simple and similar to Amazon's "users who bought X also bought Y" and can be used not only for explainable out-of-context recommendations but also for social studies on, say, which music is "closest" to users of a particular political affiliation. It also helps to introduce and to "link" a user to an unknown domain, say, politics in Tunisia

WWW '14 Companion

ABSTRACT Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011-April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-indifferences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article