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INVESTIGATING CRIME-TO-TWITTER RELATIONSHIPS IN URBAN ENVIRONMENTS – FACILITATING A VIRTUAL
"... Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood ..."
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Social networks offer vast potential for marketing agencies, as members freely provide private information, for instance on their current situation, opinions, tastes, and feelings. The use of social networks to feed into crime platforms has been acknowledged to build a kind of a virtual neighborhood watch. Current attempts that tried to automatically connect news from social networks with crime platforms have concentrated on documentation of past events, but neglected the opportunity to use
Finding Influential Neighbors to Maximize Information Diffusion in Twitter
"... The problem of spreading information is a topic of consid-erable recent interest, but the traditional influence maxi-mization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbi-trary k ..."
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The problem of spreading information is a topic of consid-erable recent interest, but the traditional influence maxi-mization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbi-trary k nodes in a network, we have developed a decentral-ized version of the influential maximization problem by influ-encing k neighbors rather than arbitrary users in the entire network. We present several reasonable neighbor selection schemes and evaluate their performance with a real dataset collected from Twitter. Unlike previous studies using net-work topology alone or synthetic parameters, we use real propagation rate for each node calculated from the Twitter messages during the 2010 UK election campaign. Our ex-perimental results show that information can be efficiently propagated in online social networks using neighbors with a high propagation rate rather than those with a high number of neighbors.
Spatio-Temporal Signal Recovery from Political Tweets in Indonesia
"... 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 par ..."
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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.
An Integrated Model For User Attribute Discovery: A Case Study on Political Affiliation Identification
"... Abstract. Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be gen-eralized to include three components — users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effe ..."
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Abstract. Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be gen-eralized to include three components — users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an in-tegrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affilia-tion identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1 % for user party detection task.
Framework for Participative and Collaborative Governance using Social Media Mining Techniques
"... Social media and networking sites have broken the barriers in communication and brought about a revolution in information access, dissemination and communication. Given the inclusive and collaborative nature of social media, political leaders and government organizations too have attempted to harnes ..."
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Social media and networking sites have broken the barriers in communication and brought about a revolution in information access, dissemination and communication. Given the inclusive and collaborative nature of social media, political leaders and government organizations too have attempted to harness it for building support, gauging popularity and analyzing the opinions of citizens. This paper, proposes a framework which focuses on the application of text mining techniques on social media content for achieving participative, collaborative and inclusive governance. Most of the political entities and government departments have twitter accounts. Though the tweet text is limited to 140 characters, the accompanying metadata and hashtag (#tag) embedded content make it a potent source for extracting entities, concepts and topics. Web Crawling for content pertaining to these entities and compilation of the same, results in a corpus suitable for text mining. Applying Natural Language Processing (NLP) and mining techniques like Part-of-Speech (POS) tagging, classification, clustering to this generated corpus enables categorizing and summarizing: a) sentiments b) queries and c) grievances. Summarized view of the content makes it amenable for decision making and formulation of responses. A Knowledgebase (KB) is created and perpetually updated with the concepts, entities, summaries, queries and responses. The continued process of creation and enhancement contributes to machine learning. The scope of this paper is limited to proposing a conceptual framework for social media mining facilitating informed decision making for better governance.
