A mixture of positive (friendly) and negative (antagonistic) relations exist among users in most social media applications. However, many such applications do not allow users to explicitly express the polarity of their interactions. As a result most research has either ignored negative links or was limited to the few domains where such relations are explicitly expressed (e.g. Epinions trust/distrust). We study text exchanged between users in online communities. We find that the polarity of the links between users can be predicted with high accuracy given the text they exchange. This allows us to build a signed network representation of discussions; where every edge has a sign: positive to denote a friendly relation, or negative to denote an antagonistic relation. We also connect our analysis to social psychology theories of balance. We show that the automatically predicted networks are consistent with those theories. Inspired by that, we present a technique for identifying subgroups in discussions by partitioning singed networks representing them.
REF proposed to predict the polarity of interactions between users based on their textual exchanges.
Detecting Subgroups in Online Discussions by Modeling Positive and Negative Relations among Participants
EMNLP
Computer Science