Animal tool use is of inherent interest given its relationship to intelligence, innovation and cultural behaviour. Here we investigate whether shark Bay bottlenose dolphins that use marine sponges as hunting tools (spongers) are culturally distinct from other dolphins in the population based on the criteria that sponging is both socially learned and distinguishes between groups. We use social network analysis to determine social preferences among 36 spongers and 69 non-spongers sampled over a 22-year period while controlling for location, sex and matrilineal relatedness. Homophily (the tendency to associate with similar others) based on tool-using status was evident in every analysis, although maternal kinship, sex and location also contributed to social preference. Female spongers were more cliquish and preferentially associated with other spongers over non-spongers. Like humans who preferentially associate with others who share their subculture, tool-using dolphins prefer others like themselves, strongly suggesting that sponge tool-use is a cultural behaviour.

BACKGROUND: While researchers have studied negative professional consequences of medical trainee social media use, little is known about how medical students informally use social media for education and career development. This knowledge may help future and current physicians succeed in the digital age. OBJECTIVE: We aimed to explore how and why medical students use Twitter for professional development. DESIGN: This was a digital ethnography. PARTICIPANTS: Medical student Bsuperusers^of Twitter participated in the study APPROACH: The postings (Btweets^) of 31 medical student superusers were observed for 8 months (May-December 2013), and structured field notes recorded. Through purposive sampling, individual key informant interviews were conducted to explore Twitter use and values until thematic saturation was reached (ten students). Three faculty key informant interviews were also conducted. Ego network and subnetwork analysis of student key informants was performed. Qualitative analysis included inductive coding of field notes and interviews, triangulation of data, and analytic memos in an iterative process. KEY RESULTS: Twitter served as a professional tool that supplemented the traditional medical school experience. Superusers approached their use of Twitter with purpose and were mindful of online professionalism as well as of being good Twitter citizens. Their tweets reflected a mix of personal and professional content. Student key informants had a high number of followers. The subnetwork of key informants was well-connected, showing evidence of a social network versus information network. Twitter provided value in two major domains: access and voice. Students gained access to information, to experts, to a variety of perspectives including patient and public perspectives, and to communities of support. They also gained a platform for advocacy, control of their digital footprint, and a sense of equalization within the medical hierarchy. CONCLUSIONS: Twitter can serve as a professional tool that supplements traditional education. Students' practices and guiding principles can serve as best practices for other students as well as faculty.

This article investigates the prevalence of high and low quality URLs shared on Twitter when users discuss COVID-19. We distinguish between high quality health sources, traditional news sources, and low quality misinformation sources. We find that misinformation, in terms of tweets containing URLs from low quality misinformation websites, is shared at a higher rate than tweets containing URLs on high quality health information websites. However, both are a relatively small proportion of the overall conversation. In contrast, news sources are shared at a much higher rate. These findings lead us to analyze the network created by the URLs referenced on the webpages shared by Twitter users. When looking at the combined network formed by all three of the source types, we find that the high quality health information network, the low quality misinformation network, and the news information network are all well connected with a clear community structure. While high and low quality sites do have connections to each other, the connections to and from news sources are more common, highlighting the central brokerage role news sources play in this information ecosystem. Our findings suggest that while low quality URLs are not extensively shared in the COVID-19 Twitter conversation, a well connected community of low quality COVID-19 related information has emerged on the web, and both health and news sources are connecting to this community.

BackgroundTwitter is becoming an important tool in medicine, but there is little information on Twitter metrics. In order to recommend best practices for information dissemination and diffusion, it is important to first study and analyze the networks.ObjectiveThis study describes the characteristics of four medical networks, analyzes their theoretical dissemination potential, their actual dissemination, and the propagation and distribution of tweets.MethodsOpen Twitter data was used to characterize four networks: the American Medical Association (AMA), the American Academy of Family Physicians (AAFP), the American Academy of Pediatrics (AAP), and the American College of Physicians (ACP). Data were collected between July 2012 and September 2012. Visualization was used to understand the follower overlap between the groups. Actual flow of the tweets for each group was assessed. Tweets were examined using Topsy, a Twitter data aggregator.ResultsThe theoretical information dissemination potential for the groups is large. A collective community is emerging, where large percentages of individuals are following more than one of the groups. The overlap across groups is small, indicating a limited amount of community cohesion and cross-fertilization. The AMA followers’ network is not as active as the other networks. The AMA posted the largest number of tweets while the AAP posted the fewest. The number of retweets for each organization was low indicating dissemination that is far below its potential.ConclusionsTo increase the dissemination potential, medical groups should develop a more cohesive community of shared followers. Tweet content must be engaging to provide a hook for retweeting and reaching potential audience. Next steps call for content analysis, assessment of the behavior and actions of the messengers and the recipients, and a larger-scale study that considers other medical groups using Twitter.

Background: Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Results: Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Conclusion: Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.