Smart crowdsourcing based content review system (SCCRS): An approach to improve trustworthiness of online contents


Online media is now a significant carrier for quicker and ubiquitous diffusion of information. Any user in social media can post contents, provide news blogs, and engage in debate or opinion nowadays. Most of the posted pieces of information on social media are useful while some are fallacious and insulting to others. Keeping the promise of freedom of speech and simultaneously no tolerance against hate speech often becomes a challenge for the hosting services. Some automated tools were developed for content filtering in industries. Also, companies are hiring specialized reviewers for accurate and unbiased reporting. However, these approaches are not achieving the goal as expected, on the other hand, new strategies are being adopted to tweak the automated systems. To face the situation, we proposed a smart crowdsourcing based content review technique to provide trustworthy and unbiased reviews for online shared contents. In this techniques, we designed an intelligent self-learned crowdsourcing strategy to select an appropriate set of reviewers efficiently which ensures reviewers’ diversity, availability, quality, and familiarity with the news topic. To evaluate our proposed method, we developed a mobile app similar to popular social media (e.g., Facebook).

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)