Business engagement on Twitter: a path analysis

Social media services, such as Twitter, enable commercial businesses to participate actively in online word-of-mouth communication. In this project, we examined the potential influences of business engagement in online word-of-mouth communication on the level of consumers’ engagement and investigated the trajectories of a business’ online word-of-mouth message diffusion in the Twitter community. We used path analysis to examine 164,478 tweets from 96,725 individual Twitter users with regards to nine brands during a 5-week study period. We operationalized business engagement as the amount of online word-of-mouth messages from brand and the number of consumers the brand follows. We operationalized consumers’ engagement as the number of online word-of-mouth messages from consumers both connecting to the brand and having no connection with the brand as well as the number of consumers following the brand. We concluded that the business engagement on Twitter relates directly to consumers’ engagement with online word-of-mouth communication. In addition, retweeting, as an explicit way to show consumers’ response to business engagement, indicates that the influence only reaches consumers with a second-degree relationship to the brand and that the life cycle of a tweet is generally 1.5 to 4 hours at most. Our research has critical implications in terms of advancing the understanding of the business’s role in the online word-of-mouth communication and bringing insight to the analytics of social networks and online word-of-mouth message diffusion patterns.     

Brand followers’ retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth

Twitter, the popular microblogging site, has received increasing attention as a unique communication tool that facilitates electronic word-of-mouth (eWOM). To gain greater insight into this potential, this study investigates how consumers’ relationships with brands influence their engagement in retweeting brand messages on Twitter. Data from a survey of 315 Korean consumers who currently follow brands on Twitter show that those who retweet brand messages outscore those who do not on brand identification, brand trust, community commitment, community membership intention, Twitter usage frequency, and total number of postings.     

Civil Society in Times of Crisis: Understanding Collective Action Dynamics in Digitally‐Enabled Volunteer Networks

Social media play an important role in political mobilization. Voluntary engagement can especially benefit from new opportunities for organizing collective action. Although research has explored the use of Twitter by decentralized individuals for this, there has been little emphasis on its use for community engagement and the provision of public goods. Even less is known about its role in the emergence and offline expansion of spontaneous self‐organized solidarity initiatives. This paper investigates how networked communication facilitates self‐organization and the development of ties in a network of volunteers in Greece. To examine whether initiative‐specific community feelings that can transcend online‐offlsine divides evolve in such hybrid networks, the analysis is complemented with individual‐level data drawn from a survey with the initiative's volunteers.     

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.     

Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others

Twitter is an Internet social-network and micro-blogging platform with both mass and interpersonal communication features for sharing 140-character messages, called tweets, with other people, called followers. Hierarchical OLS regression of survey results from 317 Twitter users found that the more months a person is active on Twitter and the more hours per week the person spends on Twitter, the more the person gratifies a need for an informal sense of camaraderie, called connection, with other users. Controlling for demographic variables does not diminish this positive relationship. Additionally, frequency of tweeting and number of @replies, public messages between Twitter users, mediate the relationship between active Twitter use and gratifying a need for connection. Results are discussed in light of uses and gratifications theory.     

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.     

Being a Serial Transnational Activist

Transnational activism endures as a political practice turning a mirror onto the world's powerbrokers. We analyse a variety of transnational activism best characterized as serial by virtue of an observed systematic time and border‐spanning commitment to protest communication. Following statistical disambiguation of a dataset of 2.5 million unique Twitter users, we identified a subset of exceptionally prolific communicators and interviewed 21 of them. We show that a noted prominence in networked communication of otherwise unremarkable Twitter users may be an upshot of purposive strategies intended to publicize, support or help orchestrate collective action. Accordingly, we propose the term “engagement compass” to address the relationship between activists' life‐patterns and their personal investment in protest over time.     

The relationship between passive and active non-political social media use and political expression on Facebook and Twitter

Departing from the conventional approach that emphasizes civic and political motives for political engagement, this study investigates how political social media behaviors—political expression—might emerge out of everyday, non-political use of the sites from an interpersonal communication perspective. Using two separate adult samples of Facebook (n = 727) and Twitter users (n = 663), this study examines how non-political, passive (NPP, consuming non-political content) and non-political, active (NPA, producing non-political content) social media use relate to expression of political voice on the sites. Findings show that only NPA use is positively associated with increased political expression, and this relationship is partially explained by political efficacy. The patterns of findings are consistent across Facebook and Twitter.     

The effect of Twitter on college student engagement and grades

Despite the widespread use of social media by students and its increased use by instructors, very little empirical evidence is available concerning the impact of social media use on student learning and engagement. This paper describes our semester‐long experimental study to determine if using Twitter – the microblogging and social networking platform most amenable to ongoing, public dialogue – for educationally relevant purposes can impact college student engagement and grades. A total of 125 students taking a first year seminar course for pre‐health professional majors participated in this study (70 in the experimental group and 55 in the control group). With the experimental group, Twitter was used for various types of academic and co‐curricular discussions. Engagement was quantified by using a 19‐item scale based on the National Survey of Student Engagement. To assess differences in engagement and grades, we used mixed effects analysis of variance (ANOVA) models, with class sections nested within treatment groups. We also conducted content analyses of samples of Twitter exchanges. The ANOVA results showed that the experimental group had a significantly greater increase in engagement than the control group, as well as higher semester grade point averages. Analyses of Twitter communications showed that students and faculty were both highly engaged in the learning process in ways that transcended traditional classroom activities. This study provides experimental evidence that Twitter can be used as an educational tool to help engage students and to mobilize faculty into a more active and participatory role.     

Exploring the U.S. Coast Guard's stance agility on Twitter during Hurricane Harvey

Drawing on the contingency theory of strategic conflict management, this study examines how the U.S. Coast Guard (USCG) used Twitter to communicate during Hurricane Harvey. A content analysis was conducted on tweets (N = 273) by using stances along the contingency's continuum, finding that the USCG frequently adopted stances on both sides of the continuum's spectrum throughout the disaster event. The analysis of USCG tweets provides a real‐world case study of how Twitter is used by organizations to strategically communicate with the public during a disaster event. Like many disaster aid organizations, the USCG must balance competing goals of assisting the impacted population, while continuing to secure funding from the U.S. government. The current study contributes to contingency theory of strategic conflict management literature by applying the theory to a disaster, providing tangible evidence that disaster communication research should consider incorporating different stances across the continuum, sometimes simultaneously, that could help guide strategies, ultimately helping the organization and public being affected during a disaster.     

Public attention to extreme weather as reflected by social media activity

Recent advancements in the development of information and communication technologies have revolutionized risk and crisis communication. This research explored how social media facilitates information seeking, interpretation, and dissemination during extreme weather. Using Twitter data collected during a tornado‐warned storm, this study explored the activity of different actor groups. The findings demonstrate that weather professionals and weather enthusiasts acted as “key stewards” who facilitated discussion during the event. Citizens engaged in the dialogue predominately by retweeting and by sharing personal observations of the storm. The results highlight the usefulness of Twitter for the propagation of both official and unofficial storm‐related information. This study also supports previous research that suggests that tweet activity may be a reliable indicator of public attention.     

Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations

In this paper we present a methodology to analyze and visualize streams of Social Media messages and apply it to a case in which Twitter is used as a backchannel, i.e. as a communication medium through which participants follow an event in the real world as it unfolds. Unlike other methods based on social networks or theories of information diffusion, we do not assume proximity or a pre-existing social structure to model content generation and diffusion by distributed users; instead we refer to concepts and theories from discourse psychology and conversational analysis to track online interaction and discover how people collectively make sense of novel events through micro-blogging. In particular, the proposed methodology extracts concept maps from twitter streams and uses a mix of sentiment and topological metrics computed over the extracted concept maps to build visual devices and display the conversational flow represented as a trajectory through time of automatically extracted topics. We evaluated the proposed method through data collected from the analysis of Twitter users’ reactions to the March 2015 Apple Keynote during which the company announced the official launch of several new products.     

“I Have AIDS”: Content analysis of postings in HIV/AIDS support group on a Chinese microblog

With the widespread growth and adoption of new technologies, online platforms such as social network sites (SNSs) have become a channel for health information. Online groups have been set up for communication and interaction, among which many are for people with chronic illnesses, including people with HIV/AIDS (PHA). In the study of online communication behavior, the Social Information Processing Theory (SIPT) predicted people will develop deeper interpersonal relationships in online groups over time (Walther, 1996). However, the author argued that CMC content should be examined more closely to get a dynamic picture of how people interaction and how groups develop over time. Thus this paper attempted to refine SIPT by enriching the framework with detailed components, and used directed content analysis to categorize messages posted on the PHA Support Group on Sina Weibo, a China-based microblog. The results showed that the percentage of socio-emotional messages saw an increase of almost 1/3 over time, taking over informational messages as the major content in all online postings. Medical related informational messages surpassed non-medical related informational messages as time went by. Intimacy relationship messages saw drastic increase in the two time periods. This study refined SIPT by providing increased granularity of its categorization scheme to examine group communication more closely.     

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.     

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.     

Sending and receiving text messages with sexual content: Relations with early sexual activity and borderline personality features in late adolescence

This research examined adolescents' written text messages with sexual content to investigate how sexting relates to sexual activity and borderline personality features. Participants (N = 181, 85 girls) completed a measure of borderline personality features prior to 10th grade and were subsequently given smartphones configured to capture the content of their text messages. Four days of text messaging were micro-coded for content related to sex. Following 12th grade, participants reported on their sexual activity and again completed a measure of borderline personality features. Results showed that engaging in sexting at age 16 was associated with reporting an early sexual debut, having sexual intercourse experience, having multiple sex partners, and engaging in drug use in combination with sexual activity two years later. Girls engaging in sex talk were more likely to have had sexual intercourse by age 18. Text messaging about hypothetical sex in grade 10 also predicted borderline personality features at age 18. These findings suggest that sending text messages with sexual content poses risks for adolescents. Programs to prevent risky sexual activity and to promote psychological health could be enhanced by teaching adolescents to use digital communication responsibly.     

Efficient decision support for detecting content polluters on social networks: an approach based on automatic knowledge acquisition from behavioral patterns

With the rise of social networking services such as Facebook and Twitter, the problem of spam and content pollution has become more significant and intractable. Using social networking services, users are able to develop relationships and share messages with others in a very convenient manner; however, they are vulnerable to receiving spam messages. The automatic detection of spammers or content polluters on the network can effectively reduce the burden on the service provider in making a decision on appropriate counteractions. Content polluters can be automatically identified by using the supervised learning technique of artificial intelligence. To build a classification model with high accuracy automatically from the training data set, it is important to identify a set of useful features that can classify polluters and non-polluters. Moreover, because we deal with a huge amount of raw data in this process, the efficiency of data preparation and model creation are also critical issues that need to be addressed. In this paper, we present an efficient method for detecting content polluters on Twitter. Specifically, we propose a set of features that can be easily extracted from the messages and behaviors of Twitter users and construct a new breed of classifiers based on these features. The proposed approach requires only a minimal number of feature values per Twitter user and thus adds considerably less time to the overall mining process compared to other methods. Experiments confirm that the proposed approach outperforms previous approaches in both classification accuracy and processing time.     

Twitter analysis algorithms for Intelligence Circulation System

The convergence of broadcasting and broadband communications network technologies has attracted increasing attention as a means to enrich the television viewing experience of viewers. Toward this end, this study proposes the ‘Intelligence Circulation System (ICS)’, which provides several services, by using newly developed algorithms for analysing Twitter messages. Twitter users often post messages about on-air TV programmes. ICS obtains viewer responses from tweets without requiring any new infrastructure or changes in users’ habits or behaviours, and it generates and provides several outputs to heterogeneous devices based on the analysis results. The algorithms—designed by considering the characteristics of Twitter messages about TV programmes—use auxiliary programme information, similarity between messages, and time series of messages. An evaluation of our algorithms using Twitter messages about all programme genres for a month showed that the accuracy of topic extraction was 85 % for an emphasis on quality (with 56 % of messages processed) and 65 % for an emphasis on quantity (with 95 % of messages processed). The accuracy of message sentimental classification was 66 %. We also describe social recommendation services using the analysis result. We have created a Social TV site for a large-scale field trial, and we have analysed users’ behaviours by comparing four types of social recommendation services on it. The experimental result shows that active and passive communication users had different needs with regard to the recommendations. ICS can generate recommendations for satisfying the needs of both user types by using the analysis result of Twitter messages.     

The power of comments: fostering social interactions in microblog networks

Today’s ubiquitous online social networks serve multiple purposes, including social communication (Facebook, Renren), and news dissemination (Twitter). But how does a social network’s design define its functionality? Answering this would need social network providers to take a proactive role in defining and guiding user behavior. In this paper, we first take a step to answer this question with a data-driven approach, through measurement and analysis of the Sina Weibo microblogging service. Often compared to Twitter because of its format,Weibo is interesting for our analysis because it serves as a social communication tool and a platform for news dissemination, too. While similar to Twitter in functionality, Weibo provides a distinguishing feature, comments, allowing users to form threaded conversations around a single tweet. Our study focuses on this feature, and how it contributes to interactions and improves social engagement.We use analysis of comment interactions to uncover their role in social interactivity, and use comment graphs to demonstrate the structure of Weibo users interactions. Finally, we present a case study that shows the impact of comments in malicious user detection, a key application on microblogging systems. That is, using properties of comments significantly improves the accuracy in both modeling and detection of malicious users.     

A project management perspective on student's declarative commitments to goals established within asynchronous communication

Teamwork and technology, even as people are seeing their increased use in organizations, are becoming important components of problem‐based learning in academic settings. Yet, fostering computer‐assisted teamwork is complex and time consuming. Knowing how and when to intervene would prove useful. This study draws from the field of project management to explore how students commit to project goals using collective asynchronous text‐based communication technology. Declarative commitments – goal‐orientated public, voluntary, explicit and non‐retractable messages comprised of a term, an objective and a focus – made by 34 teams during a four‐phase 13‐week project were analysed qualitatively and quantitatively. Qualitative results show that declarative commitments voluntarily and formally package information about project constraints into a relatively potent message about tasks, coordination and project completion. Team members' suggestions as to what should be carried out in the project and requests for help often preceded others' declarative commitments. As with persuasive communication (i.e. aimed at changing beliefs, attitudes and behaviours), declarative commitments were followed by demands for clarification, new declarative commitments, confirmations of upheld commitments and clear approvals of what was committed to. Looking at project progression from a broader perspective, quantitative analyses show that declarative commitments did partially mediate the relationship between frequencies of task issues and of task solutions. This was particularly pronounced in the mid‐point of the project, but it was not the case during the initial or final phases of the project. Taken together, these results suggest that teachers can facilitate computer‐assisted learning and project goal attainment by monitoring asynchronous electronic discussions, and by eliciting and structuring declarative commitments.