An Analytical Framework for Social Media Content Moderation using Crowdsourcing

With the rapid growth of social media use, the numbers of user-generated posts are growing exponentially. Social media platforms find it challenging to moderate all these posts before reaching to a wider range of audience as these posts are written using multiple languages and using different forms of multimedia. Social media platforms find it difficult to detect hate speech in social media content for local languages such as Sinhala or Singlish as contextual, linguistic expertise, social and cultural insights are required for accurate hate speech identification. 

Research is being carried out in detecting hate speech in social media data in English with the help of crowdsourcing platforms. But still, it is required further research for local languages. Following this necessity, in this research, we propose a suitable crowdsourcing approach to moderate hate speech from social media content. For this, it is proposed to implement a crowdsourcing platform with mechanisms to pre-select contributors, rewarding, contributor reputation management, analytical capabilities, and moderate hate speech content. With the use of a well-implemented crowdsourcing platform, it will be possible to find more nuanced patterns with the use of human judgment and filtering and to take preventive measures to create a better cyberspace.