Collaborating Authors

Visualizing a Personal Timeline By Adding Multiple Social Contexts

AAAI Conferences

We improve the readability of a personal timeline byweaving multiple social contexts of tweets into a visualization.Our social contexts consist of three dimensions:community membership, key persons, and interestingtweets within a personal timeline. A person is oftena member of several communities, such as a family,a class, or a team, simultaneously. We identify all communitiesthat a user participates in. Labeling a tweetwith a visual representation to indicate what communityit belongs to can help readers to understand why thetweet is written, since different communities are likelyto carry tweets in different contexts. We then discoverkey persons and interesting tweets within a personaltimeline. Our prototype design demonstrates how threesocial contexts work together for visualizing a personaltimeline.

The Social World of Twitter: Topics, Geography, and Emotions

AAAI Conferences

Debate is open as to whether social media communities resemble real-life communities, and to what extent. We contribute to this discussion by testing whether established sociological theories of real-life networks hold in Twitter. In particular, for 228,359 Twitter profiles, we compute network metrics (e.g., reciprocity, structural holes, simmelian ties) that the sociological literature has found to be related to parts of one's social world (i.e., to topics, geography and emotions), and test whether these real-life associations still hold in Twitter. We find that, much like individuals in real-life communities, social brokers (those who span structural holes) are opinion leaders who tweet about diverse topics, have geographically wide networks, and express not only positive but also negative emotions. Furthermore, Twitter users who express positive (negative) emotions cluster together, to the extent of having a correlation coefficient between one's emotions and those of friends as high as 0.45. Understanding Twitter's social dynamics does not only have theoretical implications for studies of social networks but also has practical implications, including the design of self-reflecting user interfaces that make people aware of their emotions, spam detection tools, and effective marketing campaigns.

The Bursty Dynamics of the Twitter Information Network Machine Learning

In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure. These bursts transform users' networks of followers to become structurally more cohesive as well as more homogenous in terms of follower interests. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.

Why You Are More Engaged: Factors Influencing Twitter Engagement in Occupy Wall Street

AAAI Conferences

Twitter has been used for engaging with audiences online in several popular political movements. In this paper we explore factors that influence the engagement of Twitter users during the recent Occupy Wall Street movement, where engagement is measured by retweets and hashtag usage related to the movement. Through analyzing Twitter activities of more than 18,000 users, we found that users’ general activity level, geographic location, topic interests and interpersonal interactions before the movement all had measurable effects on users’ engagement level during the movement.

What Sticks With Whom? Twitter Follower-Followee Networks and News Classification

AAAI Conferences

In this paper we analyze Twitter as a news channel in which the network of followers and followees significantly corresponds with the message content. We classified our data into twelve topics analogous to traditional newspaper sections and investigated whether the spread of information depended upon the Twitter network of followers and followees. To test this, we mapped the social network related to each topic and calculated the occurrence of retweet and mention mes-sages whose senders and receivers were interconnected as followers and followees. We found that on average 10% of retweets (RT-messages) and 5% of direct mentions between users (AT-messages) in Twitter hashtags are sent and received by users interconnected as followers and followees. These figures vary considerably from topic to topic, ranging from 15%-19% within Technology, Special Events and Politics to 3%-5% within the categories Personalities and Twitter-Idioms. The results show that hard-news messages are retweeted by a considerably larger community of users interconnected as followers and followees. We then performed a statistical correlation analysis of the dataset to validate the classification of hashtag in news sections based on retweet connectivity.