A Continuous-time Mutually-Exciting Point Process Framework for Prioritizing Events in Social Media

TL;DR

This paper introduces a continuous-time multi-dimensional point process model that integrates textual, sentiment, and temporal features to effectively prioritize social media posts, outperforming existing methods.

Contribution

It presents a novel unified framework combining static and dynamic features for post prioritization in social media, capturing complex interaction patterns.

Findings

Achieves state-of-the-art performance in event prediction and prioritization.

Incorporates textual, sentiment, and temporal features for better accuracy.

Features related to user profiles and linguistic characteristics are most predictive.

Abstract

The overwhelming amount and rate of information update in online social media is making it increasingly difficult for users to allocate their attention to their topics of interest, thus there is a strong need for prioritizing news feeds. The attractiveness of a post to a user depends on many complex contextual and temporal features of the post. For instance, the contents of the post, the responsiveness of a third user, and the age of the post may all have impact. So far, these static and dynamic features has not been incorporated in a unified framework to tackle the post prioritization problem. In this paper, we propose a novel approach for prioritizing posts based on a feature modulated multi-dimensional point process. Our model is able to simultaneously capture textual and sentiment features, and temporal features such as self-excitation, mutual-excitation and bursty nature of social interaction. As an evaluation, we also curated a real-world conversational benchmark dataset crawled from Facebook. In our experiments, we demonstrate that our algorithm is able to achieve the-state-of-the-art performance in terms of analyzing, predicting, and prioritizing events. In terms of interpretability of our method, we observe that features indicating individual user profile and linguistic characteristics of the events work best for prediction and prioritization of new events.