Friday, 1:30–3:00 PM
Chair: Jure Lescovec
Inferring Relevant Social Networks from Interpersonal Communication
Munmun De Choudhury, Winter Mason, Jake Hofman, Duncan Watts
Researchers increasingly use electronic communication data to construct and study large social networks, effectively inferring unobserved ties (e.g. i is connected to j) from observed communication events (e.g. i emails j). Often overlooked, however, is the impact of tie definition on the corresponding network, and in turn the relevance of the inferred network to the research question of interest. Here we study the problem of network inference and relevance for two email data sets of different size and origin. In each case, we generate a family of networks parameterized by a threshold condition on the frequency of emails exchanged between pairs of individuals. After demonstrating that different choices of the threshold correspond to dramatically different network structures, we then formulate the relevance of these networks in terms of a series of prediction tasks that depend on various network features. In general, we find: a) that prediction accuracy is maximized over a non-trivial range of thresholds corresponding to 5–10 reciprocated emails per year; b) that for any prediction task, choosing the optimal value of the threshold yields a sizable (~30%) boost in accuracy over naive choices; and c) that the optimal threshold value appears to be (somewhat surprisingly) consistent across data sets and prediction tasks. We emphasize the practical utility in defining ties via their relevance to the prediction task(s) at hand and discuss implications of our empirical results.
What is Twitter, a Social Network or a News Media?
Haewoon Kwak, Changhyun Lee, Hosung Park, Sue Moon
Twitter is a microblogging service that has emerged as a new medium in spotlight. Its unique social features (e.g., follow and retweet), brevity of messages, and easy access via SMS have all helped Twitter users share information. In this work, we study the nature of Twitter as a social networking medium and its power as a new medium of information sharing. We have crawled the entire Twittersphere for 38 million users as of September 2009, 4,262 unique trending topics, and tweet messages about the topics during a collection period of four months. Using the massive amount of crawled data, we present an extensive analysis of Twitter. Our findings shed light on unique strengths of Twitter as a new media and possibly a social studies platform.
Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors
Takeshi Sakaki, Makoto Okazaki, Yutaka Matsuo
Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes, in Twitter, and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake by monitoring tweets with high probability (96\% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected). Our system detects earthquakes promptly and sends e-mails to registered users. In many cases, the e-mails can be received before an earthquake actually arrives. Notification is delivered much faster than the announcements that are broadcast by the JMA.