Civilians heroes: A social network analysis of information structure on social media during disaster

Online social activists through social media play an important role in disseminating information during disaster.However the nature and scale of the typical activist’s involvement with social media have remained unexplored topic.This study applies Social Network Analysis to identify the key information brokers, their roles and the type and flow of information exchanges between the online activists in the network structure.The analysis was performed on a dataset consisting of 139 posts from a voluenteer social media group. The findings demonstrate that civilians play crucial role during disaster as information broker and information boundary spanner, bridging different clusters of network.There were 5 important clusters discovered, each orchestrated around different type of supports.The different types of information posted also reflected the integrative supports covering physical, mental and supports offered for the victims.The evolution of clusters during and post period demonstrates the transitions of social media use by the civilians in coping and managing disasters.This finding can serves as a foundation for integrating public and formal efforts during disaster which ideally increase the efficiency of disaster management

#radonc: Growth of the Global Radiation Oncology Twitter Network

Introduction Social media connects people globally and may enhance access to radiation oncology information. We characterized the global growth of the radiation oncology Twitter community using the hashtag #radonc. Materials and Methods We analyzed all public tweets bearing the hashtag #radonc from 2014-2019 using Symplur Signals. We collected data on #radonc activity and growth, stakeholder distribution, user geolocation, and languages. We obtained global Twitter user data and calculated average annual growth rates for users and tweets. We analyzed growth rates by stakeholder. We conducted thematic analysis on a sample of tweets in each three-year period using frequently occurring two-word combinations. Results We identified 193,115 tweets including #radonc composed by 16,645 Twitter users. Globally, users wrote in 35 languages and came from 122 countries, with the known highest users from the United States, United Kingdom, and Spain. Use of #radonc expanded from 23 countries in 2014 to 116 in 2019. The average annual growth rate in #radonc users and tweets was 70.5% and 69.2%, respectively. The annual growth rate of #radonc users was significantly higher than for all Twitter users (p=0.004). While doctors were the source of 46.9% of all tweets, research and government organizations had annual increases in tweet volume of 84.6% and 211.4%, respectively. From 2014 to 2016, promotion of the radiation oncology community was the most active theme, though this dropped to 7th in 2017-2019 as discussion increased regarding aspects of radiation and treated disease sites. Conclusion Use of #radonc has grown rapidly into a global community. Focused discussion related to radiation oncology has outpaced the growth of general Twitter use, both among physicians and non-physicians. #radonc has grown into a self-sustaining community. Further research is necessary to define the risks and benefits of social media in medicine and to determine whether it adds value to oncology practice

Examining the information dissemination process on social media during the Malaysia 2014 floods using Social Network Analysis (SNA)

This article is based on a study which examined the information dissemination process on the social media during the Malaysia 2014 floods by employing the Social Network Analysis. Specifically, the study analyzed the type of network structure formed and its density, the influential people involved, and the kind of information shared during the flood.The data was collected from a non-governmental organization fan page (NGOFP) and a significant civilian fan page (ICFP) on Facebook using NodeXL.The two datasets contained 296 posts which generated different network structures based on the state of the flood, information available, and the needs of the information.Through content analysis, five common themes emerged from the information exchanges for both fan pages which helped in providing material and psychological support to the flood victims. However, only 5% of the networks' population served as information providers, and this prompted the need for more active participation especially from organizations with certified information. Based on the findings presented and elaborated, this article concluded by stating the implications and recommendations of the study conducted

Influence maximisation towards target users and minimal diffusion of information based on information needs

Influence maximisation within social network is essential to the modern business. Influence Maximisation Problem (IMP) involves the minimal selection of influencers that leads to maximum contagion while minimizing Diffusion Cost (DC). Previous models of IMP do not consider DC in spreading information towards target users. Furthermore, influencer selection for varying information needs was not considered which leads to influence overlaps and elimination of weak nodes. This study proposes the Information Diffusion towards Target Users (IDTU) algorithm to enhance influencer selection while minimizing the DC. IDTU was developed on greedy approach by using graph sketches to improve the selection of influencers that maximize influence spread to a set of target users. Moreover, the influencer identification based on specific needs was implemented using a General Additive Model on four fundamental centralities. Experimental method was used by employing five social network datasets including Epinions, Wiki-Vote, SlashDot, Facebook and Twitter from Stanford data repository. Evaluation on IDTU was performed against 3 greedy and 6 heuristics benchmark algorithms. IDTU identified all the specified target nodes while lowering the DC by up to 79%. In addition, the influence overlap problem was reduced by lowering up to an average of six times of the seed set size. Results showed that selecting the top influencers using a combination of metrics is effective in minimizing DC and maximizing contagion up to 77% and 32% respectively. The proposed IDTU has been able to maximize information diffusion while minimizing DC. It demonstrates a more balanced and nuanced approach regarding influencer selection. This will be useful for business and social media marketers in leveraging their promotional activities