A social network as information: The effect of system generated reports of connectedness on credibility on Twitter

Social media have gained increased usage rapidly for a variety of reasons. News and information is one such reason. The current study examines how system-generated cues available in social media impact perceptions of a source’s credibility. Participants were asked to view one of six mock Twitter.com pages that varied both the number of followers and the ratio between followers and follows on the page and report their perceived source credibility. Data indicate that curvilinear effects for number of followers exist, such that having too many or too few connections results in lower judgments of expertise and trustworthiness. Having a narrow gap between the number of followers and follows also led to increased judgments of competence. Implications of these findings are discussed, along with limitations of the current study and directions for future research.     

Some like it lots: The influence of interactivity and reliance on credibility

This study has four primary purposes: to investigate the level of interactivity with 15 sources of political information, determine the degree of reliance on each of the sources, assess perceptions of credibility, and compare the strength of interactivity to the strength of reliance on judgments of credibility. This study includes sources that have received little attention in the credibility literature such as social network sites, Twitter, and mobile device applications. Respondents interact with and rely on the sources moderately. Credibility ratings range from moderate to highly credible. Reliance predicts credibility of 14 of 15 sources as compared to interactivity that predicts credibility of just 9 of 15 sources. Interactivity with sources that are inherently collaborative (e.g. Twitter, social media, talk radio) more strongly predicts credibility than interactivity with sources that are more source-to-user based (e.g. CNN, political websites).     

Social Media as Information Source: Recency of Updates and Credibility of Information

Social media are increasingly being used as an information source, including information related to risks and crises. The current study examines how pieces of information available in social media impact perceptions of source credibility. Specifically, participants in the study were asked to view 1 of 3 mock Twitter.com pages that varied the recency with which tweets were posted and then to report on their perceived source credibility of the page owner. Data indicate that recency of tweets impacts source credibility; however, this relationship is mediated by cognitive elaboration. These data suggest many implications for theory and application, both in computer‐mediated communication and crisis communication. These implications are discussed, along with limitations of the current study and directions for future research.     

Peri-Watchdog: Hunting for hidden botnets in the periphery of online social networks

In order to evade detection of ever-improving defense techniques, modern botnet masters are constantly looking for new communication platforms for delivering C&C (Command and Control) information. Attracting their attention is the emergence of online social networks such as Twitter, as the information dissemination mechanism provided by these networks can naturally be exploited for spreading botnet C&C information, and the enormous amount of normal communications co-existing in these networks makes it a daunting task to tease out botnet C&C messages.Against this backdrop, we explore graph-theoretic techniques that aid effective monitoring of potential botnet activities in large open online social networks. Our work is based on extensive analysis of a Twitter dataset that contains more than 40 million users and 1.4 billion following relationships, and mine patterns from the Twitter network structure that can be leveraged for improving efficiency of botnet monitoring. Our analysis reveals that the static Twitter topology contains a small-sized core sugraph, after removing which, the Twitter network breaks down into small connected components, each of which can be handily monitored for potential botnet activities. Based on this observation, we propose a method called Peri-Watchdog, which computes the core of a large online social network and derives the set of nodes that are likely to pass botnet C&C information in the periphery of online social network. We analyze the time complexity of Peri-Watchdog under its normal operations. We further apply Peri-Watchdog on the Twitter graph injected with synthetic botnet structures and investigate the effectiveness of Peri-Watchdog in detecting potential C&C information from these botnets.To verify whether patterns observed from the static Twitter graph are common to other online social networks, we analyze another online social network dataset, BrightKite, which contains evolution of social graphs formed by its users in half a year. We show not only that there exists a similarly relatively small core in the BrightKite network, but also this core remains stable over the course of BrightKite evolution. We also find that to accommodate the dynamic growth of BrightKite, the core has to be updated about every 18 days under a constrained monitoring capacity.     

Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter

Due to the growth and popularity of Twitter, automated programs that can tweet are increasingly developed and employed. In line with the Computers Are Social Actors (CASA) paradigm (Reeves & Nass, 1996), findings suggest that Twitterbots are perceived as credible, attractive, competent in communication, and interactional. Additionally, there were no differences in the perceptions of source credibility, communication competence, or interactional intentions between the bot and human Twitter agents. However, the source of the human Twitter agent was rated higher in attraction (social and task) than was the Twitterbot. Results are discussed in light of CASA. Implications for organizations that might employ Twitterbots are also addressed.     

The Shot Heard Around the World Wide Web: Who Heard What Where About Osama bin Laden's Death

This study investigated what sources were relied on to find out about Osama bin Laden's death and whether perceptions of credibility and political party affiliation influenced these media choices. The most striking difference in media reliance for bin Laden news was that whatever sources Tea Partiers relied on and thought credible were those that Democrats did not rely on or see as credible. Despite the clamor about how quickly news flows through social network sites and Twitter, only about 5% of respondents first heard about bin Laden through these sources and a slightly larger percentage spread the news via these tools. Moreover, social network sites and Twitter were the least relied on about the aftermath of bin Laden's death.     

Social media metrics: Third-person perceptions of health information

The present study investigated the role of social media metrics (i.e., number of shares and comments) displayed alongside online news stories in shaping users’ perceptions of the content and its influence. In a web-based experiment (N = 144), participants first read a cancer news story that displayed either a high or a low level of social media metrics, then reported their perceived story influence on the self and others, as well as their behavioral intentions. In the low social media metrics condition, the general story influence was perceived to be stronger for others than for the self, indicative of the “third-person effect.” This effect, however, was diminished to insignificant levels in the high social media metrics condition. Further, social media metrics had an ultimate indirect effect on behavioral intentions via the third-person effect. Theoretical and practical implications of these findings are provided in the end.     

Trusting expert- versus user-generated ratings online: The role of information volume,valence, and consumer characteristics

The warranting principle, signaling theory, and theories of informational social influence suggest conditions when either user-generated information, or information originating from traditional experts, might be privileged online. A random sample of 1207 U.S.-based adults with Internet access completed an experiment that manipulated the source, volume, and valence of online movie ratings in order to test predictions derived from these perspectives. Results indicated that ratings volume is positively associated with trust of, reliance on, and confidence in user-generated content, as well as the congruence between one’s own and others’ opinions; that ratings source and volume interact to impact credibility perceptions, reliance on user-generated information, and opinion congruence, such that people tend to favor experts when there is low information volume, but favor user-generated information under conditions of high information volume; and that people’s opinions and behavioral intentions converge with the online ratings information to which they are exposed. In addition, these effects apply more strongly to people more conversant with user-generated content. Results indicate important theoretical extensions by demonstrating that social information online may be filtered through signals indicating its veracity, which may not apply equally to all social media users.     

Higher education scholars' participation and practices on Twitter

Scholars participate in online social networks for professional purposes. In such networks, learning takes the form of participation and identity formation through engagement in and contribution to networked practices. While current literature describes the possible benefits of online participation, empirical research on scholars' use of online social networks in the educational technology literature is negligible. The purpose of this paper is to understand scholars' naturalistic practices in social networks in general, and on Twitter in particular. Tweets from 45 scholars were analysed qualitatively to arrive at dominant themes describing online social network practice. Findings indicate that scholars participating on Twitter (1) shared information, resources, and media relating to their professional practice; (2) shared information about their classroom and their students; (3) requested assistance from and offered suggestions to others; (4) engaged in social commentary; (5) engaged in digital identity and impression management; (6) sought to network and make connections with others; and (7) highlighted their participation in online networks other than Twitter. These findings assist the field in understanding the emerging practice of scholarly participation in online networks.     

How demanding is social media: Understanding social media diets as a function of perceived costs and benefits – A rational actor perspective

Using the rational actor perspective as a guiding frame, this exploratory study examined individuals’ social media diet (i.e., amount, frequency, and duration of use) as a function of task load and expected goal attainment. Surveys were distributed (N = 337) focusing on Twitter and Facebook usage for informational and relational purposes, respectfully. Increased task load – conceptualized as a cognitive cost – directly negatively influenced Twitter use but only indirectly influenced Facebook use as a function of perceived benefits. Across conditions, perceived self-efficacy was negatively associated with perceived task load and positively associated with goal attainment, and goal attainment was a significant correlate of increased social media usage. Interpreted, we see that a transparent technology such as Facebook has no cognitive costs associated with its use, while an opaque technology such as Twitter seems to have a salient cognitive cost element. Further, we found that older users of Facebook were more likely to judge the channel as more cognitively demanding and themselves as having lower self-efficacy in using it. Finally, results indicated that for both Facebook and Twitter, males perceived both channels as more cognitively demanding than females. Theoretical and practical explanations and applications for these findings are presented.     

社交网络用户影响力分析ABP算法研究与应用

社交网络作为一种交往方式,已经深入人心。其用户数据在这个大数据时代蕴藏着大量的价值。随着Twitter API的开放,社交网络Twitter俨然成为一个深受欢迎的研究对象,而用户影响力更是其中的研究热点。PageRank算法计算用户影响力已经由来已久,但是它太依赖于用户之间的关注关系,排名不具备时效性。引入用户活跃度的改进PageRank算法,具备一定的时效性,但是不具有足够的说服力和准确性。研究了一种新的基于时间分布用户活跃度的ABP算法,并为不同时段的活跃度加以相应的时效权重因子。最后,以Twitter为研究对象,结合社交关系网,通过实例分析说明ABP算法更具时效性和说服力,可以比较准确地提高活跃用户的排名,降低非活跃用户排名。     

Tweeting to Feel Connected: A Model for Social Connectedness in Online Social Networks

Social connectedness is an indicator of the extent to which people can realize various network benefits and is therefore a source of social capital. Using the case of Twitter, a theoretical model of social connectedness based on the functional and structural characteristics of people's communication behavior within an online social network is developed and tested. The study investigates how social presence, social awareness, and social connectedness influence each other, and when and for whom the effects of social presence and social awareness are most strongly related to positive outcomes in social connectedness. Specifically, the study looks at the concurrent direct and moderating effect of two structural constructs characterizing people's online social network: network size and frequency of usage. The research model is tested using data (n?=?121) collected from two sources: (a) an online survey of Twitter users and (b) their usage data collected directly from Twitter. Results indicate that social awareness, social presence, and usage frequency have a direct effect on social connectedness, whereas network size has a moderating effect. Social presence is found to partially mediate the relationship between social awareness and social connectedness. The findings of the analysis are used to outline design implications for online social networks from a human–computer interaction perspective.     

Computer mediated communication for social and academic purposes: Profiles of use and University students’ gratifications

The present study approaches the Internet as a social space, where university students make use of computer mediated communication (CMC) applications, i.e. e-mail, instant messaging and social network sites, in order to satisfy social and academic needs. We focus on university students, because they represent one of the most avid groups of CMC users and additionally, because they are expected to carry their perceptions of media with them into the work place and their social life. In order to investigate this issue, we conducted an empirical research using, as a target group, a sample of students from a specific Greek University. Grounded in the “uses and gratifications” perspective, we investigated the various profiles of CMC use by students along with (a) the students’ perceptions about social and academic usefulness of CMC applications, (b) the extent up to which these perceptions are correlated with students’ prior experience with the use of CMC applications, and (c) how both of these factors can predict the frequency of present use of CMC for social or academic purposes respectively. The results reveal that although these three CMC applications constitute “functional alternatives” (media that satisfy similar needs) they are different to the degree of their “functionality” for the gratification of social and academic needs. Furthermore, the degree of CMC use by students is not correlated with the years of CMC experience, but with the profile of use that students dynamically adopt according to their daily needs and preferences. The results provide evidence for the current CMC use by university students and can be useful for the implementation of further academic policies regarding CMC use in Higher Education settings.     

Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross‐Ideology Exposure on Twitter

This study integrates network and content analyses to examine exposure to cross‐ideological political views on Twitter. We mapped the Twitter networks of 10 controversial political topics, discovered clusters – subgroups of highly self‐connected users – and coded messages and links in them for political orientation. We found that Twitter users are unlikely to be exposed to cross‐ideological content from the clusters of users they followed, as these were usually politically homogeneous. Links pointed at grassroots web pages (e.g.: blogs) more frequently than traditional media websites. Liberal messages, however, were more likely to link to traditional media. Last, we found that more specific topics of controversy had both conservative and liberal clusters, while in broader topics, dominant clusters reflected conservative sentiment.     

The Rise of Twitter in the Political Campaign: Searching for Intermedia Agenda‐Setting Effects in the Presidential Primary

Questions exist over the extent to which social media content may bypass, follow, or attract the attention of traditional media. This study sheds light on such dynamics by examining intermedia agenda‐setting effects among the Twitter feeds of the 2012 presidential primary candidates, Twitter feeds of the Republican and Democratic parties, and articles published in the nation's top newspapers. Daily issue frequencies within media were analyzed using time series analysis. A symbiotic relationship was found between agendas in Twitter posts and traditional news, with varying levels of intensity and differential time lags by issue. While traditional media follow candidates on certain topics, on others they are able to predict the political agenda on Twitter.     

Understanding online consumers’ purchase intentions: a contribution from social network theory

There is a dynamic and interconnected international setting shaped by the power of the Internet and social media. To gain more consumers, understand their behaviours and needs, and maintain closest relationships with them, businesses should understand how consumers behave in social media and how they vary in their purchase intentions. In the scope of the study, we integrate the social network theory and the theory of planned behaviour to analyse online consumers’ purchase intentions and to investigate their structural positions by analysing their friendships in social networks. We target Twitter users to conduct analysis due to Twitter's popularity in use, market penetration, and opportunity to work with open-source data. This study contributes to a better theoretical understanding of online consumers’ purchase intentions by integrating multiple theoretical perspectives. It expands the literature by considering both online consumers’ friendship network in Twitter and their individual online purchasing intentions. The study also guides e-marketers to design proper strategies for potential and current consumers and target the right sets of people in the social networks.     

What kind of #conversation is Twitter? Mining #psycholinguistic cues for emergency coordination

The information overload created by social media messages in emergency situations challenges response organizations to find targeted content and users. We aim to select useful messages by detecting the presence of conversation as an indicator of coordinated citizen action. Using simple linguistic indicators drawn from conversation analysis in social science, we model the presence of coordination in the communication landscape of Twitter¹ using a corpus of 1.5 million tweets for various disaster and non-disaster events spanning different periods, lengths of time, and varied social significance. Within replies, retweets and tweets that mention other Twitter users, we found that domain-independent, linguistic cues distinguish likely conversation from non-conversation in this online form of mediated communication. We demonstrate that these likely conversation subsets potentially contain more information than non-conversation subsets, whether or not the tweets are replies, retweets, or mention other Twitter users, as long as they reflect conversational properties. From a practical perspective, we have developed a model for trimming the candidate tweet corpus to identify a much smaller subset of data for submission to deeper, domain-dependent semantic analyses for the identification of actionable information nuggets for coordinated emergency response.     

Age differences in online social networking – A study of user profiles and the social capital divide among teenagers and older users in MySpace

The aim of this research was to investigate age differences and similarities in the use of the social networking website MySpace, to explore potential differences in social capital among older people (users over 60 years of age) compared to teenagers (users between 13 and 19 years of age). We used locally developed web crawlers to collect data from MySpace’s user profile pages, and to quantify any differences that exist in the networks of friends of older people and teenagers. Content analysis was applied to investigate differences in social activities between the two age groups on MySpace, and the way they represent themselves on their profile pages. Our findings show a social capital divide: teenagers have larger networks of friends compared to older users of MySpace. On the other hand, we found that the majority of teenage users’ friends are in their own age range (age ± 2 years), whilst older people’s networks of friends tend to have a more diverse age distribution. In addition, our results show that teenagers tend to make more use of different media (e.g. video, music) within MySpace and use more self-references and negative emotions when describing themselves on their profile compared to older people.     

Who is Who on Twitter–Spammer,Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time

Social networks once being an innoxious platform for sharing pictures and thoughts among a small online community of friends has now transformed into a powerful tool of information, activism, mobilization, and sometimes abuse. Detecting true identity of social network users is an essential step for building social media an efficient channel of communication. This paper targets the microblogging service, Twitter, as the social network of choice for investigation. It has been observed that dissipation of pornographic content and promotion of followers market are actively operational on Twitter. This clearly indicates loopholes in the Twitter’s spam detection techniques. Through this work, five types of spammers-sole spammers, pornographic users, followers market merchants, fake, and compromised profiles have been identified. For the detection purpose, data of around 1 Lakh Twitter users with their 20 million tweets has been collected. Users have been classified based on trust, user and content based features using machine learning techniques such as Bayes Net, Logistic Regression, J48, Random Forest, and AdaBoostM1. The experimental results show that Random Forest classifier is able to predict spammers with an accuracy of 92.1%. Based on these initial classification results, a novel system for real-time streaming of users for spam detection has been developed. We envision that such a system should provide an indication to Twitter users about the identity of users in real-time.     

Antecedents and consequences of trust in a social media brand: A cross-cultural study of Twitter

This study extends brand relationship theory to the context of the microblogging platform Twitter. The authors investigate the impact of Twitter trust on users’ intentions to continue using the platform and to “follow” brands that are hosted on Twitter (the trust transfer phenomenon). They also explore the role of perceived self-Twitter personality match in strengthening trust towards the Twitter brand. A cross-cultural American–Ukrainian sample allows to identify potential culture-based differences in brand personality and brand trust concepts. The results show that the positive effect of trust in Twitter on its users’ patronage intentions is robust across two cultures with diverse history and ideology. An important novel finding is the influence of trust in Twitter on patronage intentions towards the businesses hosted on Twitter. However, this relationship reaches statistical significance only in the Ukrainian sample, signaling potential differences in the trust transfer processes in different cultures. The study confirms the role of similarity in personality traits between Twitter users and the Twitter brand in engendering trust in Twitter. The salience of different personality traits in the “personality match – Twitter trust” link for different cultures suggests important implications for global marketers.