Search results

The purpose of this paper is to introduce a new viewpoint series, Monitoring the Media: Spotlight on Social Media Research, by providing an overview of the key challenges in social media research and some current initiatives in addressing them.

The paper considers publication output from disciplines dealing with social media studies and summarises the key challenges as discussed in the broader research community.

The paper suggests that challenges originate both from the interdisciplinary nature of social media research and from the ever-changing research landscape. It concludes that, whilst the community is addressing some challenges, others require more attention.

The paper summarises key challenges of social media and will be of interest to researchers in different disciplines, as well as a general audience, wanting to learn about how social media data are used for research.

Through a theoretical review of the literature, this chapter assesses the potential of different knowledge organisation systems (KOS) to support corporate knowledge management systems (KMS), namely digital libraries (DL) in companies and other institutions. Questions are framed through which the chapter discusses how classical KOS, such as nomenclatures, classification systems, thesauri and ontologies, are able to reflect explicit knowledge in sense of the Semantic Web and also introduces persons as documents along with folksonomies as a means for externalising implicit knowledge in sense of the Web 2.0.

The purpose of this paper is to analyze the tweeting behavior of 37 astrophysicists on Twitter and compares their tweeting behavior with their publication behavior and citation impact to show whether they tweet research-related topics or not.

Astrophysicists on Twitter are selected to compare their tweets with their publications from Web of Science. Different user groups are identified based on tweeting and publication frequency.

A moderate negative correlation (ρ=−0.339) is found between the number of publications and tweets per day, while retweet and citation rates do not correlate. The similarity between tweets and abstracts is very low (cos=0.081). User groups show different tweeting behavior such as retweeting and including hashtags, usernames and URLs.

The study is limited in terms of the small set of astrophysicists. Results are not necessarily representative of the entire astrophysicist community on Twitter and they most certainly do not apply to scientists in general. Future research should apply the methods to a larger set of researchers and other scientific disciplines.

To a certain extent, this study helps to understand how researchers use Twitter. The results hint at the fact that impact on Twitter can neither be equated with nor replace traditional research impact metrics. However, tweets and other so-called altmetrics might be able to reflect other impact of scientists such as public outreach and science communication.

To the best of the knowledge, this is the first in-depth study comparing researchers’ tweeting activity and behavior with scientific publication output in terms of quantity, content and impact.

The purpose of this paper from the series “Monitoring the Media: Spotlight on Social Media Research” is to look into different approaches to study uses of social media platforms – from user statistics to motivations for using specific features within a platform.

Based on a literature review some general findings on social media usage are summarized alongside with examples of user activities that are rarely studied.

The paper concludes that social media research has neglected to question the use of more recent features in social media platforms, such as Twitter favorites or Facebook hashtags, as well as the more “destructive” activities in social networking such as unfollowing.

The paper draws attention to some features of popular social media platforms which are currently understudied. It raises awareness for these specific gaps in social media research and could inspire future studies to close the gap.

Purpose – The web provides scholars with mechanisms to publish new types of outputs, including videos. Little is known about which scholarly videos are successful, however, and whether their impact can be measured to give appropriate credit to their creators. This article examines online academic videos to discover which types are popular and whether view counts could be used to judge their value.

Methodology/approach – The study uses a content analysis of YouTube videos tweeted by academics: one random sample and one popular sample.

Findings – The results show that the most popular videos produced by identifiable academics are those aimed at a general audience and which are edited rather than having a simple format. It seems that the audience for typical academic videos is so small that video production in most cases cannot be justified in terms of viewer numbers alone.

Practical implications – For the typical scholar, videos should be produced for niche audiences to support other activities rather than as an end in themselves. For dissemination videos, in contrast, view counts can be used as a good indicator of failure or popularity, although translating popularity into impact is not straightforward.

The purpose of this paper is to engage in a systematic analysis of academic research that relies on the collection and use of Twitter data, creating topology of Twitter research that details the disciplines and methods of analysis, amount of tweets and users under analysis, the methods used to collect Twitter data, and accounts of ethical considerations related to these projects.

Content analysis of 382 academic publications from 2006 to 2012 that used Twitter as their primary platform for data collection and analysis.

The analysis of over 380 scholarly publications utilizing Twitter data reveals noteworthy trends related to the growth of Twitter-based research overall, the disciplines engaged in such research, the methods of acquiring Twitter data for analysis, and emerging ethical considerations of such research.

The findings provide a benchmark analysis that must be updated with the continued growth of Twitter-based research.

The research is the first full-text systematic analysis of Twitter-based research projects, focussing on the growth in discipline and methods as well as its ethical implications. It is of value for the broader research community currently engaged in social media-based research, and will prompt reflexive evaluation of what research is occurring, how it is occurring, what is being done with Twitter data, and how researchers are addressing the ethics of Twitter-based research.

The purpose of this paper is to describe and analyse relationships and communication between Twitter actors in Swedish political conversations. More specifically, the paper aims to identify the most prominent actors, among these actors identify the sub-groups of actors with similar political affiliations, and describe and analyse the relationships and communication between these sub-groups.

Data were collected during four weeks in September 2012, using Twitter API. The material included 77,436 tweets from 10,294 Twitter actors containing the hashtag #svpol. In total, 916 prominent actors were identified and categorised according to the main political blocks, using information from their profiles. Social network analysis was utilised to map the relationships and the communication between these actors.

There was a marked dominance of the three main political blocks among the 916 most prominent actors: left block, centre-right block, and right-wing block. The results from the social network analysis suggest that while polarisation exists in both followership and re-tweet networks, actors follow and re-tweet actors from other groups. The mention network did not show any signs of polarisation. The blocks differed from each other with the right-wingers being tighter and far more active, but also more distant from the others in the followership network.

While a few papers have studied political polarisation on Twitter, this is the first to study the phenomenon using followership data, mention data, and re-tweet data.