Topical Alignment in Online Social Systems

Abstract

Understanding the dynamics of social interactions is crucial to comprehend human behavior. The emergence of online social media has enabled access to data regarding people relationships at a large scale. Twitter, specifically, is an information oriented network, with users sharing and consuming information. In this work, we study whether users tend to be in contact with people interested in similar topics, i.e., if they are topically aligned. To do so, we propose an approach based on the use of hashtags to extract information topics from Twitter messages and model users'interests. Our results show that, on average, users are connected with other users similar to them. Furthermore, we show that topical alignment provides interesting information that can eventually allow inferring users' connectivity. Our work, besides providing a way to assess the topical similarity of users, quantifies topical alignment among individuals, contributing to a better understanding of how complex social systems are structured.

Information Diffusion on Complex Networks: A Novel Approach Based on Topic Modeling and Pretopology Theory

“…First, we de¯ned the number of topics for the whole corpus based on the harmonic mean of log-likelihood (HLK). 24 We calculated HLK with the number of topics in the range [10,200] with sequence 10. We realized that the best number of topics is in the range [40,100] for Twitter network (Fig.…”

“…8 On the other hand, with the second approach, Aiello et al 9 discovered homophily from the context of tags of social networks including Flickr, Last.fm, and aNobii. Additionally, Cardoso et al 10 explored homophily from hashtags on Twitter. However, in general, these methods have not exploited the textual information related to users yet while it contains signi¯cant information for similarity analysis, for instance, based on the content of papers, we can de¯ne whether the authors research in the same narrow subject or not, or we can determine which are common interests between two users on Twitter based on their tweets.…”

Evolution of the political opinion landscape during electoral periods

“…If two hashtags highly co-occur (i.e., they frequently appear together in the same tweet) it is a reasonable hypothesis to assume a semantic association between them. Following the ideas developed in [17], we build a complex weighted network based on hashtags' cooccurrence. Then, the topics of discussion arise as communities measured on this net-work, which we detect using the OSLOM algorithm [18].…”

“…An interesting way to study the structure of opinions in Twitter exploits the hashtags chosen by the users, assuming that this choice reveals a concept that the user wishes to address. In a recent work [17], topics are defined by determining the community structure in a weighted network of hashtags, where two hashtags are connected if they appear together in the same tweet. Assuming that the coexistence of hashtags is semantically meaningful, the community structure of such network can reveal the general topics under discussion.…”

“…In this work we extend the ideas developed in [17] to a dynamical study of the rapidly evolving opinion landscape that takes place in a society during an electoral campaign. Specifically we understand by evolution of opinion the evolution of the preferences of the social actors concerning the topics discussed in the platform, and we are particularly interested in detecting whether specific groups or users synchronize around some specific topics at some given times.…”