A Temporal Psycholinguistics Approach to Identity Resolution of Social Media Users

TL;DR

This thesis presents a novel approach to resolving social media user identities across platforms by analyzing topics, sentiments, and timing of posts, with temporal methods showing generally better performance than non-temporal ones.

Contribution

It introduces a combined temporal and non-temporal analysis framework for identity resolution and evaluates the impact of temporal window size and sentiment analysis on accuracy.

Findings

Temporal approach generally outperforms non-temporal methods.

Sentiment analysis has limited impact due to data extraction issues.

The scoring model achieved 24.2% accuracy in identity matching.

Abstract

In this thesis, we propose an approach to identity resolution across social media platforms using the topics, sentiments, and timings of the posts on the platforms. After collecting the public posts of around 5000 profiles from Disqus and Twitter, we analyze their posts to match their profiles across the two platforms. We pursue both temporal and non-temporal methods in our analysis. While neither approach proves definitively superior, the temporal approach generally performs better. We found that the temporal window size influences results more than the shifting amount. On the other hand, our sentiment analysis shows that the inclusion of sentiment makes little difference, probably due to flawed data extraction methods. We also experimented with a distance-based reward-and-punishment-focused scoring model, which achieved an accuracy of 24.198% and an average rank of 158.217 out of 2525 in our collected corpus. Future work includes refining sentiment analysis by evaluating sentiments per topic, extending temporal analysis with additional phases, and improving the scoring model through weight adjustments and modified rewards.