Identifying Roles in Social Networks using Linguistic Analysis.

Text Mining;Social Network Analysis;Mining Attitude in Discussions;Mining Influence in Discussions;Computer Science;Engineering;Computer Science & Engineering

Awadallah, Ahmed Mohammed HassanJagadish, Hosagrahar V. ;

University of Michigan

关键词: Text Mining;    Social Network Analysis;    Mining Attitude in Discussions;    Mining Influence in Discussions;    Computer Science;    Engineering;    Computer Science & Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/86271/hassanam_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Social media sites have been significantly growing in the past few years. This resultedin the emergence of several communities of communicating groups, and a huge amount oftext exchanged between members of those groups. In our work, we study how linguisticanalysis techniques can be used for understanding the implicit relations that develop inon-line communities. We use this understanding to develop models that explain the processesthat govern language use and how it reveals the formation of social relations. Westudy the relation between language choices and attitude between participants and howthey may lead to or reveal antagonisms and rifts in social groups. Both positive (friendly)and negative (antagonistic) relations exist between individuals in communicating communities.Negative relations have received very little attention, when compared to positiverelations, because of the lack of an explicit notion of labeling negative relations in mostsocial computing applications. We alleviate this problem by studying text exchanged betweenparticipants to mine their attitude. Another important aspect of our research is thestudy of influence in discussions and how it affects participants’ discourse. In any debateor discussion, there are certain types of persons who influence other people and affecttheir ideas and rhetoric. We rely on natural language processing techniques to find implicitconnections between individuals that model this influence. We couple this with networkanalysis techniques for identifying the most authoritative or salient entities. We also studyhow salience evolves over time. Our work is uniquely characterized by combining linguisticfeatures and network analysis to reveal social roles in different communities. The methods we developed can find several interesting areas of applications. For example,they can be used for identifying authoritative sources in social media, finding influentialpeople in communities, mining attitude toward events and topics, detecting rifts and subgroupformation, summarizing different viewpoints with respect to some topic or entity,and many other such applications.