The conceptual and practical ethical dilemmas of using health discussion board posts as research data.

Increasing numbers of people living with a long-term health condition are putting personal health information online, including on discussion boards. Many discussion boards contain material of potential use to researchers; however, it is unclear how this information can and should be used by researchers. To date there has been no evaluation of the views of those individuals sharing health information online regarding the use of their shared information for research purposes

An Exploration of Phishing Information Sharing: A Heuristic-Systematic Approach

Phishing is an attempt to acquire sensitive information from a user by malicious means. The losses due to phishing have exceeded a trillion dollars globally. Social media has provided an alternate to sharing information about phishing online. However, very little attention has been paid to phishing information sharing on social media. In this paper, we explore the risk characteristics of phishing information on social media, and investigate its effect on people’s sharing of information regarding phishing. We address the research questions: (a) how do people decide which phishing information to share? (b) what aspects of phishing information are more or less consequential in influencing a user to share it? The findings suggest that the phishing messages that afford coping strategies, and come from users with higher credibility are likely to achieve higher level of sharing

Twitter Strategies for Web-Based Surveying : Descriptive Analysis From the International Concussion Study

BACKGROUND: Social media provides researchers with an efficient means to reach and engage with a large and diverse audience. Twitter allows for the virtual social interaction among a network of users that enables researchers to recruit and administer surveys using snowball sampling. Although using Twitter to administer surveys for research is not new, strategies to improve response rates are yet to be reported. OBJECTIVE: To compare the potential and actual reach of 2 Twitter accounts that administered a Web-based concussion survey to rugby players and trainers using 2 distinct Twitter-targeting strategies. Furthermore, the study sought to determine the likelihood of receiving a retweet based on the time of the day and day of the week of posting. METHODS: A survey based on previous concussion research was exported to a Web-based survey website Survey Monkey. The survey comprised 2 questionnaires, one for players, and one for those involved in the game (eg, coaches and athletic trainers). The Web-based survey was administered using 2 existing Twitter accounts, with each account executing a distinct targeting strategy. A list of potential Twitter accounts to target was drawn up, together with a list of predesigned tweets. The list of accounts to target was divided into 'High-Profile' and 'Low-Profile', based on each accounts' position to attract publicity with a high social interaction potential. The potential reach (number of followers of the targeted account), and actual reach (number of retweets received by each post) between the 2 strategies were compared. The number of retweets received by each account was further analyzed to understand when the most likely time of day, and day of the week, a retweet would be received. RESULTS: The number of retweets received by a Twitter account decreased by 72% when using the 'high-profile strategy' compared with the 'low-profile strategy' (incidence rate ratio (IRR); 0.28, 95% confidence interval (CI) 0.21-0.37, P.001) and 6 PM to 11:59 PM (IRR 1.48, 95% CI 1.05-2.09, P>.05) were significantly increased relative to 6 AM to 11:59 AM. However, posting tweets during the hours of 12 PM to 5:59 PM, decreased the IRR for retweets by 40% (IRR 0.60, 95% CI 0.46-0.79, P<.001) compared with 6 AM to 11:59 AM. Posting on a Monday (IRR 3.57, 95% CI 2.50-5.09, P<.001) or Wednesday (IRR 1.50, 95% CI 1.11-1.11, P<.01) significantly increased the IRR compared with posting on a Thursday. CONCLUSIONS: Surveys are a useful tool to measure the knowledge, attitudes, and behaviors of a given population. Strategies to improve Twitter engagement include targeting low-profile accounts, posting tweets in the morning (12 AM-11:59 AM) or late evenings (6 PM-11:59 PM), and posting on Mondays and Wednesdays

Social media as an e-health communication channel: the use of (@medtweetmyhq) among students of UiTM Melaka / Wan Azfarozza Wan Athmar... [et al.]

Social media are now acknowledged as one of the platforms for engaging e-health that contributing to serious discussion and information regarding on medical and health issues. However, there are challenges and risks associated with social media in medical and health care which is misinformation. Misinformation can spread quickly on Twitter and each retweet is exposing to wider audiences. The aim of this paper is to identify the use of @MedTweetMYHQ among its’ users sepcifically among UiTM Melaka students. The researchers used in-depth interviews to five informants based on purposive sampling. The data was analysed using thematic analysis. Four themes emerged from the analysis which are the use of @MedTweetMYHQ to receive updated useful information on health, to share information on healthy lifestyle, to debunk health myths and as a platform for health discussion

Understanding Healthcare Knowledge Diffusion in WeChat

Social media such as We Chat provide new ways of communicating healthcare information and knowledge. Many healthcare institutions leverage We Chat public platform to disseminate healthcare knowledge in the hope of attracting public attention. It is critical for them to build a comprehensive understanding of the factors affecting WeChat users’ willingness to diffuse healthcare knowledge, an issue that has seldom been studied in the literature. This research aims to address this gap. Drawing on prior research on word-of-mouth, we develop a research model by integrating six factors regarding three key elements of healthcare knowledge communications: content (interestingness, usefulness, emotionality and positivity), source (source credibility) and channel (institution-based trust). The research model will be tested through a scenario-based online survey. This research is expected to contribute by (1) integrating factors that determine healthcare knowledge diffusion including the factors about content, source and channel, especially including institution-based trust as an important determinant, (2) examining the diffusion of healthcare knowledge and taking WeChat as the research context, and (3) using survey with subjective measurements to test a more comprehensive model. Potential practical implications are offered for healthcare organizations and practitioners

Political communication of Hungarian parties in social networking platforms

In recent years, social media platforms are said to have a major impact on communication and communication technologies. More specifically, popular social networking platforms are increasingly employed in political context. Thus, this study examines the online performance of activities and approaches for political communication between Hungarian political parties and civilians in social networking platforms, video hosting services, as well as microblogging services. In order to examine these connections, the author conducted a web-based quantitate analysis and a semantic sentiment analysis to calculate the efficiency and sentiment of social media posts created by political parties. According to the research results, Hungarian political parties underutilize the inherent communication potential of social networking platforms, especially on YouTube and Twitter

Analysis of content strategies of selected brand tweets and its influence on information diffusion

Purpose The purpose of this paper is to design organization message content strategies and analyse their information diffusion on the microblogging website, Twitter. Design/methodology/approach Using data from 29 brands and 9392 tweets, message strategies on twitter are classified into four strategies. Using content analysis all the tweets are classified into informational strategy, transformational strategy, interactional strategy and promotional strategy. Additionally, the information diffusion for the developed message strategies was explored. Furthermore, message content features such as text readability features, language features, Twitter-specific features, vividness features on information diffusion are analysed across message strategies. Additionally, the interaction between message strategies and message features was carried out. Findings Finding reveals that informational strategies were the dominant message strategy on Twitter. The influence of text readability features language features, Twitter-specific features, vividness features that influenced information diffusion varied across four message strategies. Originality/value This study offers a completely novel way for effectively analysing information diffusion for branded tweets on Twitter and can show a path to both researchers and practitioners for the development of successful social media marketing strategies

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Spillover Effects of Management Companies in the Vtuber Market

학위논문(석사) -- 서울대학교대학원 : 경영대학 경영학과, 2021.8. 윤혜리.Increasing usage of social media has given subsequent birth to micro-celebrities, or social media influencers (SMIs). Despite the fact that SMIs function as key opinion-leaders in society and the market, little is known about what traits make an SMI popular in the first place. While SMIs are generally considered to gain popularity from rock-bottom through individual endeavors alone, we find an exceptional media sector consisting of virtual YouTubers (vtubers). A vtuber, unlike the usual human YouTuber, is an artificially created figure strictly managed by sponsoring companies from the beginning of his/her debut. Finding a similarity between sponsor-vtuber relationships and parent-child relationships within brand extensions, we ran a random effects model against 560 company-owned vtubers to check whether similar spillover effects can be observed in a social media context as well. Our research yielded positive results, suggesting the existence of persistent spillover effects based on parent-brand popularity. An additional time series analysis was conducted against the weekly changes in the size of management agency influence on their affiliated vtubers. An ARIMA(1,2,0) model demonstrates a high fit with our data, and we find that the model confirms a constantly decreasing size of influence along with the passage of time.소셜미디어의 확산은 마이크로셀레브리티와 소셜미디어 인플루언서(SMI)의 등장을 초래했다. 이미 사회적, 경제적으로 SMI들이 오피니언 리더로서 큰 영향력을 행사하고 있음에도 불구하고 이들이 정확히 어떤 근본적 요인으로 인해 대중적 인기를 얻게 되었는지에 대해 알려진 바는 많지 않다. 많은 경우에 SMI들이 순수하게 자력으로만 팬덤을 구축하는 것으로 간주되는 것에 반해, 필자들은 버츄얼 유튜버(vtuber) 업계로부터 예외적인 상황을 목격했다. 일반적인 인간 유튜버와 달리, vtuber는 데뷔 이전부터 소속사로부터 엄격하게 관리당하고 통제 받는 가상의 디지털 캐릭터들이다. 본 연구에서는 소속사 대 vtuber의 관계가 브랜드 확장 상태의 모브랜드 대 신규 브랜드의 관계와 유사하다는 점에 착안하여, 후자의 경우에 관찰되는 스필오버 효과가 전자에서도 발현되는지 검증하기 위해 소속사와 계약을 맺고 있는 총 560 명의 vtuber에 대해 임의효과 모형을 적용시킨다. 그 결과, 소속사의 영향력이 vtuber의 인기에 대해 긍정적 스필오버 효과가 있음이 확인되었다. 또, 주차별 스필오버 효과 크기의 변화에 대한 시계열 분석을 통해 추세를 예측하는 데 적합한 모형으로 ARIMA(1,2,0) 모델을 특정해내어 시간이 지남에 따라 스필오버 효과가 감소하는 경향성을 지님을 검증했다.Chapter 1. Introduction 1 Chapter 2. Literature Review 3 Chapter 3. Research Model and Hypotheses 14 Chapter 4. Data Analysis and Methodology 18 Chapter 5. Results 21 Chapter 6. Discussion and Conclusion 29 Bibliography 32 Abstract in Korean 41 Appendices 42석