Identifying Roles in Social Networks Using Linguistic Analysis
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Social media sites have been significantly growing in the past few years. This resulted in the emergence of several communities of communicating groups, and a huge amount of text exchanged between members of those groups. In our work, we study the plausibility of using linguistic analysis techniques for understanding the implicit relations that develop inon-line communities. We develop models that explain processes that govern language
use and how it reveals the formation of social relations. More specifically, we develop methods to automatically mine attitude from on-line discussions, and to automatically model influence and salience of participants in discussions.
We study the relation between language choices and attitude between participants and how they 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 positive relations, because of the lack of an explicit notion of labeling negative relations in most social computing applications. We alleviate this problem by studying text exchanged between participants to mine their attitude.
Another important aspect of our research is the study of influence in discussions and how it is reflected on participants discourse. In any debate or discussion, there are certain types of persons who influence other people and affect their ideas and rhetoric. We rely on natural language processing techniques to find implicit connections between individuals that model how they influence each other. We couple this with network analysis
techniques for identifying the most authoritative or salient entities. We also study how salience evolves over time.
Our work is uniquely characterized by combining linguistic features and network analysis to reveal social roles and relations in 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 influential people in communities, mining attitude toward events and topics, detecting rifts and subgroup formation, summarizing different viewpoints with respect to some topic or entity, and many other such applications.