Altmetrics may be able to help in evaluating societal reach, but research significance must be peer reviewed.

Social media indicators of scholarly communication, or commonly referenced as altmetrics, are still far from being adopted as part of everyday research evaluation, but they already have stated value in indicating what is interesting and popular. Kim Holmberg argues these indicators have exciting potential for measuring the impact of public outreach. But further research is necessary to fully understand their value and possible applications. Where do we draw the line between promoting our own work and gaming the altmetrics

The Meaning of Altmetrics

A range of quantitative methods are today widely used in research evaluation (e.g. Moed et al., 1985; Moed et al., 1995). Recently, with the increasing popularity of social media, and especially the increasing use of social media in scholarly activities, a new field of research has been introduced, namely altmetrics, to investigate the use of social media in research evaluation (Priem & Hemminger, 2010). Although altmetrics does not yet have a widely accepted definition, the idea with altmetrics is that the mentions and other indicators of visibility and awareness a research article and other research products get in social media could tell something about the impact or influence of that research. Earlier altmetric research have in fact found some indications that the social media visibility of a scientific article correlates with more traditional measures of research impact, such as citations, hinting at the value of altmetrics as a rapid source of data about research impact and its potential as a tool for research evaluation (e.g. Bar-Ilan et al., 2012; Mohammadi & Thelwall, 2013; Thelwall et al., 2013a). However, little research has focused on the underlying meaning and actual validity of altmetrics. Are altmetrics measures of impact of research and, if not, what do they measure? How is attention in social media created? Does visibility in social media mean the same thing as citation impact? This paper will review some of the earlier research on the topic and based on the earlier findings discuss challenges with altmetrics and the underlying meaning and validity of altmetrics

Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior

This paper analyzes 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 (p=-0.390*) 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 our 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.Comment: 14 pages, 5 figures, 7 table

Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific papers deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. We present preliminary findings that automated Twitter accounts create a considerable amount of tweets to scientific papers and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose differentiating between different levels of engagement from tweeting only bibliographic information to discussing or commenting on the content of a paper.Comment: 9 pages, 4 figures, 1 tabl

Eigenfactor

The Eigenfactor™ is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on ResearchAssessment (DORA)

Climate change on Twitter: topics, communities and conversations about the 2013 IPCC report

In September 2013 the Intergovernmental Panel on Climate Change published its first comprehensive assessment of physical climate science in six years, constituting a critical event in the societal debate about climate change. This paper analyses the nature of this debate in one public forum: Twitter. Using webometric methods, tweets were analyzed to discover the hashtags used when people tweeted about the IPCC report, and how Twitter users formed communities around their conversational connections. In short, the paper presents the topics and tweeters at this particular moment in the climate debate. The most used hashtags related to themes of science, geographical location and social issues connected to climate change. Particularly noteworthy were tweets connected to Australian politics, US politics, geoengineering and fracking. Three communities of Twitter users were identified. Researcher coding of Twitter users showed how these varied according to geographical location and whether users were convinced or critical of climate science or policy in their Twitter usage. Overall, users were most likely to converse with users holding similar views. However, two communities displayed significant links between climate convinced and critical users, suggesting that those engaged in the climate debate were exposed to views contrasting with their own

Influence of Course Type on Upper Body Muscle Activity in Elite Cross-Country and Downhill Mountain Bikers During Off Road Downhill Cycling

This study aimed to investigate upper body muscle activity using surface electromyography (sEMG) in elite cross-country (XCO) and downhill (DH) cyclists during off road descending and the influence of man-made (MM) and natural terrain (NT) descents on muscle activity. Twelve male elite mountain bikers (n=6 XCO; age 23 ± 4 yrs; stature 180.5 ± 5.6 cm; body mass 70.0 ± 6.4 kg and n=6 DH; age 20 ± 2 yrs; stature 178.8 ± 3.1 cm; body mass 75.0 ± 3.0 kg) took part in this study. sEMG were recorded from the left biceps brachii, triceps brachii, latissimus dorsi and brachioradialis muscles and expressed as a percentage of maximal voluntary isometric contraction (% MVIC). Both groups performed single runs on different MM and NT courses specific to their cycling modality. Significant differences in mean % MVIC were found between biceps brachii and triceps brachii (p=.016) and triceps brachii and latissimus dorsi (p=.046) during MM descents and between biceps brachii and triceps brachii (p=.008) and triceps brachii and latissimus dorsi (p=.031) during NT descents within the DH group. Significant differences in mean % MVIC were found between biceps brachii and brachioradialis (p=.022) for MM runs and between biceps brachii and brachioradialis (p=.013) for NT runs within the XCO group. Upper body muscle activity differs according to the type of downhill terrain, and appears to be specific to DH and XCO riders. Therefore, the discipline specific impact on muscle activation and the type of course terrain ridden should be considered when mountain bikers engage in upper body conditioning programmes

Den första ISSOME-konferensen

Katsauksessa esitellään Turussa 24.-26. elokuuta 2011 pidettyä ensimmäistä ISSOME-konferenssia (Information Science and Social Media