Search, Structure, and Sentiment: A Comparative Analysis of Network Opinion in Different Query Types on Twitter

TL;DR

This study analyzes how the structure of Twitter conversations relates to sentiment across different query types, revealing that the inverse relationship varies significantly depending on the query focus.

Contribution

It introduces a comparative analysis of the structure-sentiment relationship across four Twitter query types, highlighting differences and methodological advancements.

Findings

Thread sentiment is inversely proportional to network strength and connectivity.

The structure-sentiment relationship varies across query types.

Most clear in individual queries, least in event-based queries.

Abstract

Understanding the relationship between structure and sentiment is essential in highlighting future operations with online social networks. More specifically, within popular conversation on Twitter. This paper provides a development on the relationship between the two variables: structure, defined as the composition of a directed network, and sentiment, a quantified value of the positive/negative connotations of a conversation. We highlight thread sentiment to be inversely proportional to the strength and connectivity of a network. The second portion of this paper highlights differences in query types, specifically how the aforementioned behavior differs within four key query types. This paper focuses on topical, event-based, geographic, and individual queries as orientations which have differing behavior. Using cross-query analysis, we see that the relationship between structure and sentiment, though still inversely proportional, differs greatly across query types. We find this relationship to be the most clear within the individual queries and the least prevalent within the event-based queries. This paper provides a sociological progression in our understanding of opinion and networks, while providing a methodological advancement for future studies on similar subjects.