A Systematic Identification and Analysis of Scientists on Twitter

Metrics derived from Twitter and other social media---often referred to as altmetrics---are increasingly used to estimate the broader social impacts of scholarship. Such efforts, however, may produce highly misleading results, as the entities that participate in conversations about science on these platforms are largely unknown. For instance, if altmetric activities are generated mainly by scientists, does it really capture broader social impacts of science? Here we present a systematic approach to identifying and analyzing scientists on Twitter. Our method can identify scientists across many disciplines, without relying on external bibliographic data, and be easily adapted to identify other stakeholder groups in science. We investigate the demographics, sharing behaviors, and interconnectivity of the identified scientists. We find that Twitter has been employed by scholars across the disciplinary spectrum, with an over-representation of social and computer and information scientists; under-representation of mathematical, physical, and life scientists; and a better representation of women compared to scholarly publishing. Analysis of the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a small fraction of shared URLs are science-related. We find an assortative mixing with respect to disciplines in the networks between scientists, suggesting the maintenance of disciplinary walls in social media. Our work contributes to the literature both methodologically and conceptually---we provide new methods for disambiguating and identifying particular actors on social media and describing the behaviors of scientists, thus providing foundational information for the construction and use of indicators on the basis of social media metrics

Citizen Journalist to Activist: the Language Behind Black Lives Matter

This study explores the discourse surrounding an event in the social movement, Black Lives Matter, to gain insights into how digital literacy practices influence and mediate participation in the 21st century civil rights movement for a new generation of activist. This study examines three points of engagement, (1) the experience of the event as it is initially interpreted and shared online, (2) the intermingling of consciousness through social media, and (3) the post social media interpretation and action. Data was collected from Twitter analyzed using discourse analysis

The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

Predicting Rising Follower Counts on Twitter Using Profile Information

When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy, NY, US

Timescales of Massive Human Entrainment

The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend concepts of entrainment to the dynamics of human collective attention. We conducted a detailed investigation of the unfolding of human entrainment - as expressed by the content and patterns of hundreds of thousands of messages on Twitter - during the 2012 US presidential debates. By time locking these data sources, we quantify the impact of the unfolding debate on human attention. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an interlocutor increase the attention received. We also highlight a distinct time scale for the impact of salient moments in the debate: Mentions in social media start within 5-10 seconds after the moment; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.Comment: 20 pages, 7 figures, 6 tables, 4 supplementary figures. 2nd version revised according to peer reviewers' comments: more detailed explanation of the methods, and grounding of the hypothese

E-democracy: exploring the current stage of e-government

Governments around the world have been pressured to implement e-Government programs in order to improve the government-citizen dialogue. The authors of this article review prior literature on such efforts to find if they lead to increased democratic participation ("e-Democracy") for the affected citizens, with a focus on the key concepts of transparency, openness, and engagement. The authors find that such efforts are a starting point toward e-Democracy, but the journey is far from complete