Iman Tahamtan

Robin Haunschild, Lutz Bornmann, Devendra Potnis et al.

One way to assess a certain aspect of the value of scientific research is to measure the attention it receives on social media. While previous research has mostly focused on the "number of mentions" of scientific research on social media, the current study applies "topic networks" to measure public attention to scientific research on Twitter. Topic networks are the networks of co-occurring author keywords in scholarly publications and networks of co-occurring hashtags in the tweets mentioning those scholarly publications. This study investigates which topics in opioid scholarly publications have received public attention on Twitter. Additionally, it investigates whether the topic networks generated from the publications tweeted by all accounts (bot and non-bot accounts) differ from those generated by non-bot accounts. Our analysis is based on a set of opioid scholarly publications from 2011 to 2019 and the tweets associated with them. We use co-occurrence network analysis to generate topic networks. Results indicated that Twitter users have mostly used generic terms to discuss opioid publications, such as "Opioid," "Pain," "Addiction," "Treatment," "Analgesics," "Abuse," "Overdose," and "Disorders." Results confirm that topic networks provide a legitimate method to visualize public discussions of health-related scholarly publications and how Twitter users discuss health-related scientific research differently from the scientific community. There was a substantial overlap between the topic networks based on the tweets by all accounts and non-bot accounts. This result indicates that it might not be necessary to exclude bot accounts for generating topic networks as they have a negligible impact on the results.

Iman Tahamtan, Javad Seif

In this paper we examine the most influential resources shared on Twitter about the #MeToo movement. We also examine whether a small proportion of domain names and URLs (e.g. 20%) appear in a large number of tweets (e.g. 80%) that contain #MeToo (known as the 80/20 rule or Pareto principle). R and Python were used to analyze the data. Results demonstrated that the most frequently shared domains were twitter.com (47.20%), nytimes.com (4.42%) and youtube.com (3.69%). The most frequently shared content was a recent poll which indicated "men are afraid to mentor women after the #MeToo movement". In accordance with the Pareto principle, 8% of domain names accounted for 80% of the shared content on Twitter that contained #MeToo. This study provides a base for researchers who are interested in understanding what online resources people rely on when sharing information about online social movements (e.g. #MeToo).

Shahram Sedghi, Zeinab Shourmeij, Iman Tahamtan

Information seeking is an interactive behaviour of the end users with information systems, which occurs in a real environment known as context. Context affects information-seeking behaviour in many different ways. The purpose of this paper is to investigate the factors that potentially constitute the context of visual information seeking. We used a Straussian version of grounded theory, a qualitative approach, to conduct the study. Using a purposive sampling method, 28 subjects participated in the study. The data were analysed using open, axial and selective coding in MAXQDA software. The contextual factors influencing visual information seeking were classified into seven categories, including: user characteristics, general search features, visual search features, display of results, accessibility of results, task type and environmental factors. This study contributes to a better understanding of how people conduct searches in and interact with visual search interfaces. Results have important implications for the designers of information retrieval systems. This paper is among the pioneer studies investigating contextual factors influencing information seeking in visual information retrieval systems.