Topic-Level Opinion Influence Model(TOIM): An Investigation Using Tencent Micro-Blogging

TL;DR

This paper introduces TOIM, a probabilistic model for analyzing and predicting user opinions on Tencent Micro-Blogging by incorporating topic and social influence, validated through experiments showing improved prediction accuracy.

Contribution

The paper presents a novel Topic-Level Opinion Influence Model (TOIM) that integrates topic factors and social influence for opinion prediction on Chinese Micro-Blogging platforms.

Findings

TOIM outperforms baseline methods in opinion prediction.

CP and NCP algorithms improve recall and F1-measure.

Model effectively leverages users' historical social interactions.

Abstract

Mining user opinion from Micro-Blogging has been extensively studied on the most popular social networking sites such as Twitter and Facebook in the U.S., but few studies have been done on Micro-Blogging websites in other countries (e.g. China). In this paper, we analyze the social opinion influence on Tencent, one of the largest Micro-Blogging websites in China, endeavoring to unveil the behavior patterns of Chinese Micro-Blogging users. This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor and social direct influence in a unified probabilistic framework. Based on TOIM, two topic level opinion influence propagation and aggregation algorithms are developed to consider the indirect influence: CP (Conservative Propagation) and NCP (None Conservative Propagation). Users' historical social interaction records are leveraged by TOIM to construct their progressive opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. To evaluate and test this proposed model, an experiment was designed and a sub-dataset from Tencent Micro-Blogging was used. The experimental results show that TOIM outperforms baseline methods on predicting users' opinion. The applications of CP and NCP have no significant differences and could significantly improve recall and F1-measure of TOIM.