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.     

Political Expression and Action on Social Media: Exploring the Relationship Between Lower‐ and Higher‐Threshold Political Activities Among Twitter Users in Italy

Scholars and commentators have debated whether lower‐threshold forms of political engagement on social media should be treated as being conducive to higher‐threshold modes of political participation or a diversion from them. Drawing on an original survey of a representative sample of Italians who discussed the 2013 election on Twitter, we demonstrate that the more respondents acquire political information via social media and express themselves politically on these platforms, the more they are likely to contact politicians via e‐mail, campaign for parties and candidates using social media, and attend offline events to which they were invited online. These results suggest that lower‐threshold forms of political engagement on social media do not distract from higher‐threshold activities, but are strongly associated with them.     

A resource allocation policy for delay minimization in fetching capacitated feeds

As social media services such as Twitter and Facebook are gaining popularity, the amount of information published from those services is explosively growing. Most of them use feeds to facilitate distribution of a huge volume of content they publish. In this context, many users subscribe to feeds to acquire up-to-date information through feed aggregation services, and recent real-time search engines also increasingly utilize feeds to promptly find recent web content when it is produced. Accordingly, it is necessary for such services to effectively fetch feeds for minimizing fetching delay, while at the same time maximizing the number of fetched entries. Fetching delay is a time lag between entry publication and retrieval, which is primarily incurred by finiteness of fetching resources. In this paper, we consider a polling-based approach among the methods applicable to fetching feeds, which bases on a specific schedule for visiting feeds. While the existing polling-based approaches have focused on the allocation of fetching resources to feeds in order to either reduce the fetching delay or increase the number of fetched entries, we propose a resource allocation policy that can optimize both objectives. Extensive experiments have been carried out to evaluate the proposed model, in comparison with the existing alternative methods.     

News Recommendations from Social Media Opinion Leaders: Effects on Media Trust and Information Seeking

Polls show a strong decline in public trust of traditional news outlets; however, social media offers new avenues for receiving news content. This experiment used the Facebook API to manipulate whether a news story appeared to have been posted on Facebook by one of the respondent's real‐life Facebook friends. Results show that social media recommendations improve levels of media trust, and also make people want to follow more news from that particular media outlet in the future. Moreover, these effects are amplified when the real‐life friend sharing the story on social media is perceived as an opinion leader. Implications for democracy and the news business are discussed.     

Campaigning on Twitter: Microblogging and Online Social Networking as Campaign Tools in the 2010 General Elections in the Netherlands

The present study focuses on how candidates in the Dutch general elections of 2010 use Twitter, a popular microblogging and social networking service. Specifically the study focuses on explaining why some candidates are more likely to adopt Twitter, have larger networks, and show more reciprocation than other candidates. The innovation hypothesis, predicting that candidates from less established and smaller parties will use Twitter more extensively, is unsupported. This suggests that normalization of campaign practices is present on Twitter, not changing existing communication practices. The findings do show that being an early adopter of these new technologies is more effective than adoption shortly before Election Day.     

Temporal and spatial analysis of the media spotlight

Items featured in the news usually have a particular novelty or describe events which result in severe impact. Here, the length of time that a story remains in the media spotlight is investigated as well as the scaling with population size of the amount of attention that the media gives to stories from different cities. Based on Twitter feeds, the media coverage from the major online newspapers in Mexico is analysed over a period either side of a recent powerful earthquake. The amount of coverage given to earthquake-related stories had an initial peak and then exhibited an exponential decay, dropping by half every eight days. Furthermore, the coverage per person usually exhibits a superlinear scaling with population size, so that stories about larger cities are more likely to appear in the news. However, during the immediate post-earthquake weeks, the scaling was no longer superlinear. The observed trends can be interpreted as a fundamental switch in the emergent collective behaviour of media producers and consumers.     

College students and risk-taking behaviour on Twitter versus Facebook

The propensity of college students to post content that they know may be unacceptable to future employers or other authority figures has been well established. Yet research on this topic has tended to focus exclusively on Facebook, which is problematic for two reasons. First, many young social media users are shifting away from Facebook and towards Twitter and other services. Second, college students have changed their use of social media over time and may now be more cautious about what they post on Facebook. To address this issue, a survey-based field study was conducted to compare student comfort levels with authority figures viewing their Facebook and Twitter accounts. Specifically, undergraduate business students attending a large university in the midwest of the USA were surveyed about their Facebook and Twitter accounts. Findings indicate that college students are markedly less comfortable with authority figures viewing their Twitter accounts. Paradoxically, a great majority of the study respondents were found to have public Twitter accounts, while only a very small minority have public Facebook accounts. This finding suggests that students perceive less risk on Twitter versus Facebook or that they are writing to different imagined audiences on the two platforms. Implications include the need for further inquiry and an awareness of educators and human resources professionals about students’ current social media practices.     

融合语境分析的时序推特摘要方法

时序推特摘要是文本摘要任务中的一个重要分支,旨在从热点事件相关的海量推特流中总结出随时间演化的简要推特集,以帮助用户快速获取信息.推特作为当今最流行的社交媒体平台,其信息量爆发式的增长以及文本碎片的非结构性,使得单纯依赖文本内容的传统摘要方法不再适用.与此同时,社交媒体的新特性也为推特摘要带来了新的机遇.将推特流视作信号,剖析了其中的复杂噪声,提出融合推特流随时序变化的宏微观信号以及用户社交上下文语境信息的时序推特摘要新方法.首先,通过小波分析对推特流全局时序信息建模,实现某一关键词相关的热点子事件时间点检测;接着,融入推特流局部时序信息和用户社交信息建立推特的随机步图模型摘要框架,为每个热点子事件生成推特摘要.在算法评估过程中,对真实推特数据集进行了专家时间点和专家摘要的人工标注,实验结果表明了小波分析和融合了时序-社交上下文语境的图模型在时序推特摘要中的有效性.     

How political candidates use Twitter and the impact on votes

This study investigates the content characteristics of Twitter during an election campaign, and the relationship between candidates’ style of online campaigning (i.e., politically personalized and interactive communication) and electoral support for those candidates. Thereby, it provides a better understanding of the linkage between the use of Twitter by candidates and effects on preferential votes. Two data sources are used to examine this relationship: first, a quantitative computer-assisted as well as a manual content analysis of tweets posted by political candidates during the Dutch national elections of 2010 (N = 40,957) and second, a dataset containing the number of votes for electable political candidates during that period. The findings show that using Twitter has positive consequences for political candidates. Candidates who used Twitter during the course of the campaign received more votes than those who did not, and using Twitter in an interactive way had a positive impact as well.     

Identifying interesting Twitter contents using topical analysis

Social media platforms such as Twitter are becoming increasingly mainstream which provides valuable user-generated information by publishing and sharing contents. Identifying interesting and useful contents from large text-streams is a crucial issue in social media because many users struggle with information overload. Retweeting as a forwarding function plays an important role in information propagation where the retweet counts simply reflect a tweet’s popularity. However, the main reason for retweets may be limited to personal interests and satisfactions. In this paper, we use a topic identification as a proxy to understand a large number of tweets and to score the interestingness of an individual tweet based on its latent topics. Our assumption is that fascinating topics generate contents that may be of potential interest to a wide audience. We propose a novel topic model called Trend Sensitive-Latent Dirichlet Allocation (TS-LDA) that can efficiently extract latent topics from contents by modeling temporal trends on Twitter over time. The experimental results on real world data from Twitter demonstrate that our proposed method outperforms several other baseline methods.     

Trip distribution modeling with Twitter data

Integrating both traditional and social media data, this study compares the performance of gravity, neural network, and random forest models of commuting trip distribution in New York City. Trip distribution modeling has primarily employed traditional data sources and classical methods such as the gravity. However, with the emergence of social media during the past decade, the potential for integrating traditional and social media data while utilizing new techniques has been identified. Our findings indicate that the random forest model outperforms the traditional gravity and neural network models. Population, distance, number of Twitter users, and employment were identified as the four most influential predictors of trip distibution by the random forest model. While Twitter flows did not enhance the models' performance, the importance of the number of Twitter users at work destinations implies the potential for using social media data in travel demand modeling to improve the predictive power and accuracy.     

Communication in the face of a school crisis: Examining the volume and content of social media mentions during active shooter incidents

Little is known about the effectiveness of social media in delivering information during active shooter incidents at the P-12 level. This study analyzed social media activity that occurred during and after two active shooter events on September 30, 2014. Over 5000 social media posts from Facebook, Twitter, blogs, and mainstream news outlets were analyzed. Social media analysis outlined the scope of online communication during the first week following the incidents, revealed social media frequency, increases in conversation, misinformation, and differences between parent and student posts. Results revealed spikes in social media chatter following the release of the identities of shooters and victims. Consistent with media dependency theory and the high levels of uncertainty characteristic of the incident, users’ social media posts contained more information than affect displays during the active shooter event. Implications for scholars and P-12 administrators are discussed.     

Influence-based Twitter browsing with NavigTweet

Directed links in social media determine the flow of information and, hence, indicate a user's influence. This paper proposes a novel visual framework to explore Twitter's ‘Who Follows Who’ relationships, by browsing the friends’ network to identify key influencers based on the actual influence of the content they share. We have developed NavigTweet, a visualization tool for the influence-based exploration of Twitter network. NavigTweet embeds a force-directed algorithm to display the graph in a multi-clustered way. To assess the user experience with NavigTweet, we have conducted a pre-release qualitative pilot study. We also report on the study and results of post-release user feedback survey.     

A Survey of Techniques for Event Detection in Twitter

Twitter is among the fastest‐growing microblogging and online social networking services. Messages posted on Twitter (tweets) have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing this rich and continuous user‐generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real‐world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.     

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

The explosive growth in social networks that publish real-time content begs the question of whether their feeds can complement traditional sensors to achieve augmented sensing capabilities. One such capability is to explain anomalous sensor readings. In our previous conference paper, we built an automated anomaly clarification service, called ClariSense, with the ability to explain sensor anomalies using social network feeds (from Twitter). In this extended work, we present an enhanced anomaly explanation system that augments our base algorithm by considering both (i) the credibility of social feeds and (ii) the spatial locality of detected anomalies. The work is geared specifically for describing small-footprint anomalies, such as vehicular traffic accidents. The original system used information gain to select more informative microblog items to explain physical sensor anomalies. In this paper, we show that significant improvements are achieved in our ability to explain small-footprint anomalies by accounting for information credibility and further discriminating among high-information-gain items according to the size of their spatial footprint. Hence, items that lack sufficient corroboration and items whose spatial footprint in the blogosphere is not specific to the approximate location of the physical anomaly receive less consideration. We briefly demonstrate the workings of such a system by considering a variety of real-world anomalous events, and comparing their causes, as identified by ClariSense+, to ground truth for validation. A more systematic evaluation of this work is done using vehicular traffic anomalies. Specifically, we consider real-time traffic flow feeds shared by the California traffic system. When flow anomalies are detected, our system automatically diagnoses their root cause by correlating the anomaly with feeds on Twitter. For evaluation purposes, the identified cause is then retroactively compared to official traffic and incident reports that we take as ground truth. Results show a great correspondence between our automatically selected explanations and ground-truth data.     

ClariSense+: An enhanced traffic anomaly explanation service using social network feeds

The explosive growth in social networks that publish real-time content begs the question of whether their feeds can complement traditional sensors to achieve augmented sensing capabilities. One such capability is to explain anomalous sensor readings. In our previous conference paper, we built an automated anomaly clarification service, called ClariSense, with the ability to explain sensor anomalies using social network feeds (from Twitter). In this extended work, we present an enhanced anomaly explanation system that augments our base algorithm by considering both (i) the credibility of social feeds and (ii) the spatial locality of detected anomalies. The work is geared specifically for describing small-footprint anomalies, such as vehicular traffic accidents. The original system used information gain to select more informative microblog items to explain physical sensor anomalies. In this paper, we show that significant improvements are achieved in our ability to explain small-footprint anomalies by accounting for information credibility and further discriminating among high-information-gain items according to the size of their spatial footprint. Hence, items that lack sufficient corroboration and items whose spatial footprint in the blogosphere is not specific to the approximate location of the physical anomaly receive less consideration. We briefly demonstrate the workings of such a system by considering a variety of real-world anomalous events, and comparing their causes, as identified by ClariSense+, to ground truth for validation. A more systematic evaluation of this work is done using vehicular traffic anomalies. Specifically, we consider real-time traffic flow feeds shared by the California traffic system. When flow anomalies are detected, our system automatically diagnoses their root cause by correlating the anomaly with feeds on Twitter. For evaluation purposes, the identified cause is then retroactively compared to official traffic and incident reports that we take as ground truth. Results show a great correspondence between our automatically selected explanations and ground-truth data.     

Sentimental causal rule discovery from Twitter

Social media, especially Twitter is now one of the most popular platforms where people can freely express their opinion. However, it is difficult to extract important summary information from many millions of tweets sent every hour. In this work we propose a new concept, sentimental causal rules, and techniques for extracting sentimental causal rules from textual data sources such as Twitter which combine sentiment analysis and causal rule discovery. Sentiment analysis refers to the task of extracting public sentiment from textual data. The value in sentiment analysis lies in its ability to reflect popularly voiced perceptions that are stated in natural language. Causal rules on the other hand indicate associations between different concepts in a context where one (or several concepts) cause(s) the other(s). We believe that sentimental causal rules are an effective summarization mechanism that combine causal relations among different aspects extracted from textual data as well as the sentiment embedded in these causal relationships. In order to show the effectiveness of sentimental causal rules, we have conducted experiments on Twitter data collected on the Kurdish political issue in Turkey which has been an ongoing heated public debate for many years. Our experiments on Twitter data show that sentimental causal rule discovery is an effective method to summarize information about important aspects of an issue in Twitter which may further be used by politicians for better policy making.     

The tweets that killed a university: A case study investigating the use of traditional and social media in the closure of a state university

The 12-month discussion surrounding a regional university campus quickly evolved from a suggestion of independence, to a plan, to the ultimate closure of the university. This unique series of events at the University of South Florida Polytechnic (USFP) allows for an investigation of how various forms of media were used during this significant event that impacted college student’s education and immediate future. A campus wide survey was combined with social and online media monitoring to assess the topics, authors, and methods used during prominent discussions during and preceding the closure of USFP. Although social media played a crucial role, the most common format was Twitter and it was used almost exclusively by members of the media itself. Students instead relied on traditional sources to gather information. Additionally, students expressed their opinion utilizing classic methods, such as petitions, foregoing more modern Twitter or Facebook campaigns. It is incorrect to automatically assume younger demographic authorship or utilization of social media technology. Whereas social media use could expand even more over the next decade, identifying authorship remains critical as it is unclear how frequent social media is viewed as an official method of public discussion, especially when politics and higher education collide.     

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.     

Urban Social Media Demographics: An Exploration of Twitter Use in Major American Cities

This article explores intersections between place, race/ethnicity, and gender amongst American Twitter users and makes an argument that studying the intensity of tweets provides insights into how and why particular groups tweet. Given recent events in American political life such as the shooting in Ferguson, Missouri and the reactions by young, urban African Americans on Twitter, understanding the role of race, place, gender, and age is important. We observed the time between tweets of urban American Twitter users and explored whether the medium may be providing traditionally marginalized groups, such as young Black men, with potential avenues for mobilizing communication and access to resources.