Exploring the effect of streamed social media data variations on social network analysis

Abstract

To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses. To this end, we developed a systematic comparison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.

Are we always in strife? A longitudinal study of the echo chamber effect in the Australian Twittersphere

“…Despite the appeal of social media as a rich data source for sociological research, a number of questions and challenges remain. For example, restricted access to OSNs' data via their Application Programming Interfaces (APIs) limits the social networks built from such data (Nasim, Charbey, Prieur, & Brandes, 2016), the retrieved data may have inconsistencies (Weber et al, 2021), reproducibility of results is not always possible (Assenmacher et al, 2021), and there is a lack of robust sociological theories about social media interaction (e.g., Schroeder, 2018). That said, interactions on social media, limited in data model though they may be, provide the best portal we have to relevant data and therefore the best opportunity to understand the degree and nature of activity between particular actors at a particular time on a given topic of discussion.…”

“…Although OSNs share many features (Weber, Nasim, Mitchell, & Falzon, 2021), the openness of micro-blog platforms, such as Twitter, Parler and Gab, where one account can directly connect to any other (via, e.g., mentions, replies and retweets and their equivalents), provides the best opportunity for accounts in polarised communities to bridge the gaps. Doing so enables new and different information to flow between the communities, enabling the potential to grow consensus.…”

“…We use Twitter data in this study, as it is the longest established of the three microblogs mentioned, and has the largest and most representative user base. It also provides a freely available rich data model, which includes information on the directed interactions between accounts, resulting in an up-to-date window into the direction and degree of information and influence flow between Twitter accounts (Weber et al, 2021).…”

On the Development of an Information System for Monitoring User Opinion and its Role for the Public

“…Currently, the foreign market is represented by many tools for monitoring social networks [23], content analysis, and brand promotion. Therefore, the marketers distinguished a list of the most popular and advanced analytics platforms: Sprout social, Hubspot, Buzzsumo, Hootsuite, Brandmention, IQBuzz, and Snaplytics take an essential place.…”

Hashtag Black Lives Matter’s Tweets as Education Media Messages

“…Twitter is good for research because of the relative ease with which tweets can be gathered and collections created, as well as the built-in analytical tools, which include retweets for significant tweets, hashtags for subject matter categorization, replies as well as followers-followers for network analysis, and shortened URLs for reference analysis (Weber et al, 2021). The character limit and the relatively uniform length of each tweet in a collection also lends itself well to textual analysis, including co-word analysis.…”