Bipartite Consensus of Opinion Dynamics Through Delivering Credible Information

Author(s):  
Yao Zou ◽  
Kewei Xia
2019 ◽  
Vol 18 (2) ◽  
pp. 303-312
Author(s):  
Guang He ◽  
Jing Liu ◽  
Yanlei Wu ◽  
Jian-An Fang

2019 ◽  
Author(s):  
Irina Vartanova ◽  
Kimmo Eriksson ◽  
Pontus Strimling

2020 ◽  
Author(s):  
Ahmed Al-Rawi ◽  
Vishal Shukla

BACKGROUND In this study, we examined the activities of automated social media accounts or bots that tweet or retweet referencing #COVID-19 and #COVID19. OBJECTIVE The purpose of this study is to identify bot accounts to understand the nature of messages sent by them on COVID-19. Social media bots have been widely discussed in academic literature as some kind of moral panic mostly in relation to spreading controversial and politically polarized messages or in connection to problematic health bots (Broniatowski et al., 2018; Allem & Ferrara, 2018). The findings of this study, however, show that bots that reference COVID-19 mostly mention mainstream media and credible health sources while spreading breaking news on the pandemic or urging people to stay at home. These results align with previous research on the possible benefits, advantages, or possibilities afforded by the use of health chatbots (Brandtzaeg & Følstad, 2018; Skjuve & Brandtzæg, 2018; Kretzschmar et al., 2019; Greer et al., 2019). METHODS We used a mixed approach mostly comprised of several digital methods in this study. First, we collected 50,811,299 tweets and retweets referencing #COVID-19 and #COVID19 for a period of over two months from February 12 until April 18, 2020. We focused on these two hashtags because they are standard terms used by WHO and other official sources. From a total sample of over 50 million tweets, we used a mixed method to extract more than 185,000 messages posted by 127 bots. RESULTS Unlike the literature on health bots that associate them with anti-social activities, our findings show that the majority of these bots tweet, retweet and mention mainstream media outlets and credible official sources, promote health protection and telemedicine, and disseminate breaking news on the number of casualties and deaths caused by COVID-19. CONCLUSIONS Despite that some literature on social media bots highlight the controversial and anti-social nature of automated accounts, the findings of this study show that the majority of bots spread news on and awareness of COVID-19 risks while citing and referencing mainstream media outlets and credible health sources. We argue that there might be financial incentives behind designing some of these bots. However and if monitored and updated with credible information by health agencies themselves, we believe that bots can be useful during health crises due to their efficiency and speed in spreading valuable information, some of which is crucial for public health. CLINICALTRIAL N/A


Dementia ◽  
2021 ◽  
pp. 147130122110284
Author(s):  
Emma Wolverson ◽  
Caroline White ◽  
Rosie Dunn ◽  
Katie Cunnah ◽  
David Howe ◽  
...  

Background: Current policy emphasises the role of digital technologies in facilitating the management of long-term conditions. While digital resources have been developed for carers, there has been little attention to their development for people with dementia. The Caregiverspro-MMD website was developed as a joint resource for people with dementia and carers, delivering access to information, informal content, games and peer support. Research Design and Methods: This study explored the experiences of dyads consisting of people with dementia and carers of using the website. Interviews and focus groups were conducted with 43 participants. Findings: Thematic analysis identified 10 subthemes grouped under three superordinate themes which highlight participants’ experiences of and responses to the website functions; important aspects of the website design and delivery; and barriers to use. Discussion: Findings highlight the value of a credible information source which negated the need for arduous online searches, the pleasure associated with playing games and interacting with others online. However, participants were reluctant to share personal information online, preferring to create ‘informal content’ which celebrated everyday life, and were reluctant to ‘friend’ people online who they had not met in person. The importance of training and support to use the website was highlighted. Health problems, lack of interest or difficulties using technology, and time were all identified as barriers to use.


2021 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Rediet Abebe ◽  
T.-H. HUBERT Chan ◽  
Jon Kleinberg ◽  
Zhibin Liang ◽  
David Parkes ◽  
...  

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.


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