Assessment of Effectiveness of Content Models for Approximating Twitter Social Connection Structures

TL;DR

This study evaluates how well different content models like unigram, bigram, and LDA can approximate Twitter social connection structures based on user-generated content, revealing that unigram models best preserve community properties.

Contribution

The paper provides an empirical comparison of content models for social link prediction on Twitter, highlighting the superior performance of unigram models in maintaining community structures.

Findings

Unigram models outperform bigram and LDA in preserving social communities.

Finer-grained semantic content improves link prediction accuracy.

Word usage within communities converges, supporting community-specific language evolution.

Abstract

This paper explores the social quality (goodness) of community structures formed across Twitter users, where social links within the structures are estimated based upon semantic properties of user-generated content (corpus). We examined the overlap of the community structures of the constructed graphs, and followership-based social communities, to find the social goodness of the links constructed. Unigram, bigram and LDA content models were empirically investigated for evaluation of effectiveness, as approximators of underlying social graphs, such that they maintain the {\it community} social property. Impact of content at varying granularities, for the purpose of predicting links while retaining the social community structures, was investigated. 100 discussion topics, spanning over 10 Twitter events, were used for experiments. The unigram language model performed the best, indicating strong similarity of word usage within deeply connected social communities. This observation agrees with the phenomenon of evolution of word usage behavior, that transform individuals belonging to the same community tending to choose the same words, made by Danescu et al. (2013), and raises a question on the literature that use, without validation, LDA for content-based social link prediction over other content models. Also, semantically finer-grained content was observed to be more effective compared to coarser-grained content.