Spatio-temporal Signal Recovery from Political Tweets in Indonesia

Anisha Mazumder, Arun Das, Nyunsu Kim, Sedat Gokalp, Arunabha Sen, H. Davulcu
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引用次数: 5

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 becoming increasingly clear that analysis of such data can provide great insights on the social, political and cultural aspects 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.
印尼政治推文的时空信号恢复
在线社交网络社区为分析人类对人物、地点、事件、政治活动的情感提供了大量数据。越来越清楚的是,对这些数据的分析可以提供对这些网络参与者的社会、政治和文化方面的深刻见解。作为Minerva项目的一部分,目前正在亚利桑那州立大学进行,我们分析了大量Twitter数据,以了解印度尼西亚各省的激进政治活动。基于对Twitter用户在推特上表达的激进/反激进情绪的分析,我们制作了一张印尼的热图,直观地展示了印尼各省激进活动的程度。我们通过计算(i)激进化指数(Radicalization Index)和(ii)每一位在推文中表达激进情绪的印尼推特用户的位置指数(Location Index)来绘制印尼热点地图。我们的分析得出的结论与印尼主要政治智库瓦希德研究所的分析结果非常吻合,从而验证了我们的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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