Twitter-a microblogging service that enables users to post messages ("tweets")

No abstract

Does social media reflect meaningful political competition over foreign policy? If so, what relationships can it reveal, and what are the limitations of its usage as data for scholars? These questions are of interest to both scholars and policymakers alike, as social media, and the data derived from it, play an increasingly important role in politics. The current study uses social media data to examine how foreign policy discussions about Israel-Iran are structured across different languages (English, Farsi, and Arabic) -a particularly contentious foreign policy issue. We use follower relationships on Twitter to build a map of the different networks of foreign policy discussions around Iran and Israel, along with data from the Iranian and Arabic blogosphere. Using social network analysis, we show that some foreign policy networks (English and Farsi Twitter networks) accurately reflect policy positions and salient cleavages (online behavior maps onto offline behavior). Others (Hebrew Twitter network) do not. We also show that there are significant differences in salience across languages (Farsi and Arabic). Our analysis accomplishes two things. First, we show how scholars can use social media data and network analysis to make meaningful inferences about foreign policy issues. Second, and perhaps more importantly, we also outline pitfalls and incorrect inferences that may result if scholars are not careful in their application.

Social media activity in different geographic regions can expose a varied set of temporal patterns. We study and characterize diurnal patterns in social media data for different urban areas, with the goal of providing context and framing for reasoning about such patterns at different scales. Using one of the largest datasets to date of Twitter content associated with different locations, we examine within-day variability and across-day variability of diurnal keyword patterns for different locations. We show that only a few cities currently provide the magnitude of content needed to support such across-day variability analysis for more than a few keywords. Nevertheless, within-day diurnal variability can help in comparing activities and finding similarities between cities.

In this study, we develop methods to identify verbal expressions in social media streams that refer to real-world activities. Using aggregate daily patterns of Foursquare checkins, our methods extract similar patterns from Twitter, extending the amount of available content while preserving high relevance. We devise and test several methods to extract such content, using time-series and semantic similarity. Evaluating on key activity categories available from Foursquare (coffee, food, shopping and nightlife), we show that our extraction methods are able to capture equivalent patterns in Twitter. By examining rudimentary categories of activity such as nightlife, food or shopping we peek at the fundamental rhythm of human behavior and observe when it is disrupted. We use data compiled during the abnormal conditions in New York City throughout Hurricane Sandy to examine the outcome of our methods.