Identifying preferred routes of sharing information on social networks

TL;DR

This paper investigates the non-random patterns of information spread on social networks, proposing models that explain how news and hashtags propagate along specific paths.

Contribution

It introduces two preferential models for understanding information dissemination, validated with real-world hashtag data and Twitter political hashtag analysis.

Findings

Hashtag spread follows discernible patterns rather than randomness.

Information tends to propagate along consistent paths influenced by content type.

Political hashtags on Twitter confirm the existence of these preferential paths.

Abstract

The spread of information has become faster and wider than ever with the advent of social network platforms. The question raised in this study is whether information dissemination in social networks is random or follows a discernible structure. Our results from real-world hashtag data suggest that the spread of hashtags is not random and follows specific patterns. This study proposes two preferential models to explore how news spreads on social media. Specifically, we examine global and local preferential selection models and demonstrate that information dissemination aligns with these patterns. According to these two models, information flows are distributed through specific paths on networks. This suggests that new information tends to propagate along the same paths as previous news, with the specific pathways varying depending on the type of content. Finally, an examination of the propagation of political hashtags on Twitter confirms the existence of these paths that also emerge from the two preferential models.