A crowdsourcing method for online social networks security assessment based on human-centric computing

Abstract

Crowdsourcing and crowd computing are a trend that is likely to be increasingly popular, and there remain a number of research and operational challenges that need to be addressed. The human-centric computational abstraction called situation may be used to cope with these difficulties. In this paper, we focus on one such challenge, which is how to assign crowd assessment tasks about security and privacy in online social networks to the most appropriate users efficiently, effectively and accurately. Specifically, here we propose a novel task assignment method to facilitate crowd assessment, which improves the security and trustworthiness of social networking platforms, as well as a task assignment algorithm based on SocialSitu, which is a social-domain-focused situational analytics. Findings from our crowd assessment experiments on a real world social network Shareteches show that the precision and recall of the proposed method and algorithm are 0.491 and 0.538 higher than those of a random algorithm’s, as well as 0.336 and 0.366 higher than users’ theme-aware algorithm’s, respectively. Moreover, these results further suggest that our experimental evaluation enhance the security and privacy of online social networks.

Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective

“…In contrast, in the case of responding to the survey, users might be attentive and an element of bias may creep in the responses. We, therefore, can safely assume that social media data contains unbiased data (in terms of intrinsic behaviour of the user), which is also supported by the studies (e.g., Bharati & Chaudhury, 2019;Meire et al, 2017;Zhang et al, 2020). However, a recent article on a survey of the social media analytics literature revealed that social media data is a source of high-quality information.…”

“…The literature suggests that there has been a focus on investigating social media data for demand forecasting, impact on morale enhancement, customer empowerment, intention to purchase and disaster management (Iftikhar & Khan, 2020; Johnston et al, 2013; Nisar & Whitehead, 2016; See-To & Ho, 2014; Wu & Cui, 2018). Social media data reflects the actual behavior of users rather than the declarations by the respondents (Bharati & Chaudhury, 2019; Zhang et al, 2020). Nowadays, both primary survey data and secondary data directly crawled from social networking sites have been used by the researchers.…”

Sensitizing Netizen’s behavior through influencer intervention enabled by crowdsourcing – a case of reddit

“…Recent studies conducted by Zhang and Gupta (2018), Kim, Gupta, andRho (2018), andZhang et al (2020) propose a model for providing digital security to users by crowdsourcing the entities from the existing pool of users. Similarly, the authors propose the selection of influencers from the pool of Redditors based on the number of posts generated by them and the number of comments received for each post.…”

Exploring the synergy between nano-influencers and sports community: behavior mapping through machine learning

“…Nano-influencers could act as a liaison between brands and customers and may hold the potential to influence customer sentiments, however the selection of the right nano-influencer is a challenge for organizations (Belanche et al, 2021;Lee and Eastin, 2021;Sun et al, 2021). To help them select the right influencer from the pool, we have proposed the scheme developed by Zhang et al (2020) that is based on crowdsourcing to identify the users to assess the Nanoinfluencers and sports community privacy and security in social networks. We have defined task-related factors and their attributes.…”