Association for Information Science and Technology 

About: Association for Information Science and Technology is an academic conference. The conference publishes majorly in the area(s): Information behavior & Information seeking. Over the lifetime, 2820 publications have been published by the conference receiving 40959 citations.

The sharing economy: Why people participate in collaborative consumption

Abstract: Information and communications technologies ICTs have enabled the rise of so-called "Collaborative Consumption" CC: the peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services. CC has been expected to alleviate societal problems such as hyper-consumption, pollution, and poverty by lowering the cost of economic coordination within communities. However, beyond anecdotal evidence, there is a dearth of understanding why people participate in CC. Therefore, in this article we investigate people's motivations to participate in CC. The study employs survey data N=168 gathered from people registered onto a CC site. The results show that participation in CC is motivated by many factors such as its sustainability, enjoyment of the activity as well as economic gains. An interesting detail in the result is that sustainability is not directly associated with participation unless it is at the same time also associated with positive attitudes towards CC. This suggests that sustainability might only be an important factor for those people for whom ecological consumption is important. Furthermore, the results suggest that in CC an attitude-behavior gap might exist; people perceive the activity positively and say good things about it, but this good attitude does not necessary translate into action.

Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references

Abstract: Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an advanced statistical technique—segmented regression analysis—which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.

Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake

Abstract: The US response to the 2010 Haiti Earthquake was a large effort coordinated by three major agencies that worked in tandem with the Government of Haiti, the United Nations, and many countries from around the globe. Managing this response effort was a complex undertaking that relied extensively on knowledge management systems (KMS). For the first time, however, US government agencies employed social media technologies such as wikis and collaborative workspaces as the main knowledge sharing mechanisms. In this research we present a case study developed through action research of how these social media technologies were used, what influences they made on knowledge sharing, reuse, and decision-making, and how knowledge was effectively (and at times ineffectively) maintained in these systems. First-hand knowledge of the response is used, offering strategies for future deployment of social media and important research questions that remain regarding social media as knowledge management systems, particularly for disaster and emergency management.

Automatic deception detection: methods for finding fake news

Abstract: This research surveys the current state-of-the-art technologies that are instrumental in the adoption and development of fake news detection. "Fake news detection" is defined as the task of categorizing news along a continuum of veracity, with an associated measure of certainty. Veracity is compromised by the occurrence of intentional deceptions. The nature of online news publication has changed, such that traditional fact checking and vetting from potential deception is impossible against the flood arising from content generators, as well as various formats and genres. The paper provides a typology of several varieties of veracity assessment methods emerging from two major categories -- linguistic cue approaches (with machine learning), and network analysis approaches. We see promise in an innovative hybrid approach that combines linguistic cue and machine learning, with network-based behavioral data. Although designing a fake news detector is not a straightforward problem, we propose operational guidelines for a feasible fake news detecting system.

Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature

Abstract: Data collected by social media platforms have been introduced as new sources for indicators to help measure the impact of scholarly research in ways that are complementary to traditional citation analysis. Data generated from social media activities can be used to reflect broad types of impact. This article aims to provide systematic evidence about how often Twitter is used to disseminate information about journal articles in the biomedical sciences. The analysis is based on 1.4 million documents covered by both PubMed and Web of Science and published between 2010 and 2012. The number of tweets containing links to these documents was analyzed and compared to citations to evaluate the degree to which certain journals, disciplines, and specialties were represented on Twitter and how far tweets correlate with citation impact. With less than 10% of PubMed articles mentioned on Twitter, its uptake is low in general but differs between journals and specialties. Correlations between tweets and citations are low, implying that impact metrics based on tweets are different from those based on citations. A framework using the coverage of articles and the correlation between Twitter mentions and citations is proposed to facilitate the evaluation of novel social-media-based metrics.