Two types of well followed users in the followership networks of Twitter

In the Twitter blogosphere, the number of followers is probably the most basic and succinct quantity for measuring popularity of users. However, the number of followers can be manipulated in various ways; we can even buy follows. Therefore, alternative popularity measures for Twitter users on the basis of, for example, users' tweets and retweets, have been developed. In the present work, we take a purely network approach to this fundamental question. First, we find that two relatively distinct types of users possessing a large number of followers exist, in particular for Japanese, Russian, and Korean users among the seven language groups that we examined. A first type of user follows a small number of other users. A second type of user follows approximately the same number of other users as the number of follows that the user receives. Then, we compare local (i.e., egocentric) followership networks around the two types of users with many followers. We show that the second type, which is presumably uninfluential users despite its large number of followers, is characterized by high link reciprocity, a large number of friends (i.e., those whom a user follows) for the followers, followers' high link reciprocity, large clustering coefficient, large fraction of the second type of users among the followers, and a small PageRank. Our network-based results support that the number of followers used alone is a misleading measure of user's popularity. We propose that the number of friends, which is simple to measure, also helps us to assess the popularity of Twitter users.Comment: 4 Figures and 8 Table

Teens, Social Media, and Privacy

Teens share a wide range of information about themselves on social media sites; indeed the sites themselves are designed to encourage the sharing of information and the expansion of networks. However, few teens embrace a fully public approach to social media. Instead, they take an array of steps to restrict and prune their profiles, and their patterns of reputation management on social media vary greatly according to their gender and network size

Elite Tweets: Analysing the Twitter Communication Patterns of Labour Party Peers in the House of Lords

The micro-blogging platform Twitter has gained notoriety for its status as both a communication channel between private individuals, and as a public forum monitored by journalists, the public, and the state. Its potential application for political communication has not gone unnoticed; politicians have used Twitter to attract voters, interact with constituencies and advance issue-based campaigns. This article reports on the preliminary results of the research team’s work with 21 peers sitting on the Labour frontbench. It is based on the monitoring and archival of the peers’ activity on Twitter for a period of 100 days from 16th May to 28th September 2012. Using a sample of more than 4,363 tweets and a mixed methodology combining semantic analysis, social network analysis and quantitative analysis, this paper explores the peers’ patterns of usage and communication on Twitter. Key findings are that as a tweeting community their behavior is consistent with others, however there is evidence that a coherent strategy is lacking. Labour peers tend to work in ego networks of self-interest as opposed to working together to promote party polic

Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific papers deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. We present preliminary findings that automated Twitter accounts create a considerable amount of tweets to scientific papers and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose differentiating between different levels of engagement from tweeting only bibliographic information to discussing or commenting on the content of a paper.Comment: 9 pages, 4 figures, 1 tabl

Reverse Engineering Socialbot Infiltration Strategies in Twitter

Data extracted from social networks like Twitter are increasingly being used to build applications and services that mine and summarize public reactions to events, such as traffic monitoring platforms, identification of epidemic outbreaks, and public perception about people and brands. However, such services are vulnerable to attacks from socialbots $-$ automated accounts that mimic real users $-$ seeking to tamper statistics by posting messages generated automatically and interacting with legitimate users. Potentially, if created in large scale, socialbots could be used to bias or even invalidate many existing services, by infiltrating the social networks and acquiring trust of other users with time. This study aims at understanding infiltration strategies of socialbots in the Twitter microblogging platform. To this end, we create 120 socialbot accounts with different characteristics and strategies (e.g., gender specified in the profile, how active they are, the method used to generate their tweets, and the group of users they interact with), and investigate the extent to which these bots are able to infiltrate the Twitter social network. Our results show that even socialbots employing simple automated mechanisms are able to successfully infiltrate the network. Additionally, using a $2^k$ factorial design, we quantify infiltration effectiveness of different bot strategies. Our analysis unveils findings that are key for the design of detection and counter measurements approaches

White, Man, and Highly Followed: Gender and Race Inequalities in Twitter

Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic factor. Despite numerous efforts that explore demographic factors in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this paper, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. Then, we investigate how different demographic groups (i.e. male/female, Asian/Black/White) connect with other. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. Our analysis shows that users identified as White and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in user's lists. We hope our effort can stimulate the development of new theories of demographic information in the online space.Comment: In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'17). Leipzig, Germany. August 201

Mapping networks of influence: tracking Twitter conversations through time and space

The increasing use of social media around global news events, such as the London Olympics in 2012, raises questions for international broadcasters about how to engage with users via social media in order to best achieve their individual missions. Twitter is a highly diverse social network whose conversations are multi-directional involving individual users, political and cultural actors, athletes and a range of media professionals. In so doing, users form networks of influence via their interactions affecting the ways that information is shared about specific global events. This article attempts to understand how networks of influence are formed among Twitter users, and the relative influence of global news media organisations and information providers in the Twittersphere during such global news events. We build an analysis around a set of tweets collected during the 2012 London Olympics. To understand how different users influence the conversations across Twitter, we compare three types of accounts: those belonging to a number of well-known athletes, those belonging to some well-known commentators employed by the BBC, and a number of corporate accounts belonging to the BBC World Service and the official London Twitter account. We look at the data from two perspectives. First, to understand the structure of the social groupings formed among Twitter users, we use a network analysis to model social groupings in the Twittersphere across time and space. Second, to assess the influence of individual tweets, we investigate the ageing factor of tweets, which measures how long users continue to interact with a particular tweet after it is originally posted. We consider what the profile of particular tweets from corporate and athletes’ accounts can tell us about how networks of influence are forged and maintained. We use these analyses to answer the questions: How do different types of accounts help shape the social networks? and, What determines the level and type of influence of a particular account

Why Do You Spread This Message? Understanding Users Sentiment in Social Media Campaigns

Twitter has been increasingly used for spreading messages about campaigns. Such campaigns try to gain followers through their Twitter accounts, influence the followers and spread messages through them. In this paper, we explore the relationship between followers sentiment towards the campaign topic and their rate of retweeting of messages generated by the campaign. Our analysis with followers of multiple social-media campaigns found statistical significant correlations between such sentiment and retweeting rate. Based on our analysis, we have conducted an online intervention study among the followers of different social-media campaigns. Our study shows that targeting followers based on their sentiment towards the campaign can give higher retweet rate than a number of other baseline approaches