Detecting and analyzing bursty events on Twitter

This thesis presents BurstMapper, a system for detecting and characterizing bursts of tweets generated by multiple sources in order to understand interactions between Twitter users and the role of exogenous events (not directly observable on Twitter) in driving tweets. The first stage of the system finds temporal clusters, or bursts of tweets. The second stage characterizes bursts along two dimensions, semantic coherence and causal influence. Semantic coherence measures the semantic relatedness of the tweets in a burst to each other based on a deep neural network derived embedding of tweet contents. Causal influence measures the potential causal interaction between Twitter users using the Hawkes process model. We introduce an annotated corpus of 7,220 tweets produced by five leading candidates in the 2016 U.S. presidential election. Evaluating the system on the annotated corpus shows that with a precision of 75%, tweets caused clearly by specific exogenous events (or responsive tweets hereafter) are detected by the burst detector components of our system. Furthermore, experiments show that the linear combination of semantic coherence and causal influence are predictive of the presence of responsive tweets in a burst, with the Fl-score of 0.76. Examining bursts along the two dimensions reveals that (i) the measures are positively correlated with each other (corr=0.33, p<0.001), (ii) the measures allow us to understand how candidates tend to respond differently to exogenous events, e.g., by attacking opponents or making plan announcements, and (iii) the measures can be used to describe the influence dynamics between candidates over time. Plotting the bursts from a corpus of 1,470 Twitter accounts (the five leading candidates and the users followed by them) shows visual evidence that some user groups (e.g., campaign staffs, journalists, etc.) have a higher levels of semantic coherence and causal interactions. These experiments suggest that the bursts detected by our system provide a useful level of abstraction that summarizes tweet content, providing a solution for coping with massive amount of data on Twitter.

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Kung, Pau Perng-Hwa

Advisor dc:contributor.advisor

• Deb Roy.

• Program in Media Arts and Sciences ()

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/107558

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/107558