Spatio-temporal Signal Recovery from Political Tweets in Indonesia

Abstract

Abstract-Online social network community now provides an enormous volume of data for analyzing human sentiment about people, places, events and political activities. It is increasingly clear that analysis of such data can provide great insights on the social, political and cultural aspect of the participants of these networks. As part of the Minerva project, currently underway at Arizona State University, we have analyzed a large volume of Twitter data to understand radical political activity in the provinces of Indonesia. Based on analysis of radical/counter radical sentiments expressed in tweets by Twitter users, we create a Heat Map of Indonesia which visually demonstrates the degree of radical activities in various provinces of Indonesia. We create the Heat Map of Indonesia by computing (i) the Radicalization Index and (ii) the Location Index of each Twitter user from Indonesia, who has expressed some radical sentiment in her tweets. The conclusions derived from our analysis matches significantly with the analysis of Wahid Institute, a leading political think tank of Indonesia, thus validating our results.

User characterization for online social networks

“…Mazumder et al [51] state that online social networks provide large amount of information about human feelings and emotion about people, places, events, and political activities. As part of the Minerva project [51] at Arizona State University, information has been collected to understand political activities of Indonesia. Based on these data, a heat map is generated for the radical activities in provinces of Indonesia by computing radicalization index and location index of each Twitter user from Indonesia.…”

Spatiotemporal Data Warehousing

“…Signal Recovery Signal recovery extracts ground truth observations from noisy, incomplete social media data. For example, Mazumder et al (2013) analyzes the political tweets in Indonesia to recover the degree of radical activities. Xu et al (2013) recovers the wildlife surveillance statistics from animal road-killing tweets.…”

Smil

“…Signal Recovery Signal recovery extracts ground truth observations from noisy, incomplete social media data. For example, Mazumder et al (2013) analyzes the political tweets in Indonesia to recover the degree of radical activities. Xu et al (2013) recovers the wildlife surveillance statistics from animal road-killing tweets.…”