Elites Tweet? Characterizing the Twitter Verified User Network

Abstract

Social network and publishing platforms, such as Twitter, support the concept of verification. Verified accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tantamount to endorsement. However, a significant body of prior work suggests that possessing a verified status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a status is highly coveted among public figures and influencers. Hence, we attempt to characterize the network of verified users on Twitter and compare the results to similar analysis performed for the entire Twitter network. We extracted the entire network of verified users on Twitter (as of July 2018) and obtained 231,246 English user profiles and 79,213,811 connections. Subsequently, in the network analysis, we found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects such as possessing a short diameter. However, our findings contrast with earlier findings on multiple aspects, such as the possession of a power law outdegree distribution, slight dissortativity, and a significantly higher reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient components within this sub-graph and detect the absence of homophily with respect to popularity, which again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series of verified user activity levels. To the best of our knowledge, this work represents the first quantitative attempt at characterizing verified users on Twitter.

A Computational Approach to Analyzing the Twitter Debate on Gaming Disorder

“…This includes accounts maintained by users in music, acting, fashion, government, politics, religion, journalism, media, sports, [and] business." This relatively high number (compared to less than 1% verified users in a random sample) suggests a high involvement of journalistic actors and actors who are otherwise involved in public life (Paul, Khattar, Kumaraguru, Gupta, & Chopra, 2019).…”

Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19

“…In both CD and ND, the 25-34 bin comprises the biggest part, which is consistent with the age distribution of general 11. https://greatdata.com/product/urban-vs-rural Twitter users. 12 After performing the goodness-of-fit test, we find that the age distributions in these two groups are statistically different (p < 0.0001). The Twitter users in ND tend to be younger.…”

“…Gong et al studied the silent users in social media communities and found that user generated content can be used for profiling silent users [11]. Paul et al were the first to utilize network analysis to characterize the Twitter verified user network [12]. Users consciously deleting tweets were observed by Bhattacharya et al [28].…”

#AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling

“…Although the social media-based analytical methods of collective actions have accelerated the slow and expensive conventional methods, such as surveys, they are still characterized by a variety of deficiencies, such as lack of representativeness or transferability of the results. Moreover, the widespread and well-established advantages of network modeling approaches on hashtags [13,14,28,29] or users [30,31] in recent literature analyzing protests based social media data, they are often not capable of handling the complexity of the sentiment, temporal, and spatial patterns of these actions in one approach. One limitation is that the analysis relies on Tweets having coordinates as an inherent part of the dataset [32,33], which-according to earlier studies-represents only a small subset of all tweets (approximately 1-10%) posted within a specified time period [34].…”