Cassandra: a crowdsourced testbed for content assessment of potential social media posts

ABSTRACT

Content assessment of posts before broadcasting them in social media has become crucial for many social media users. Primary reasons include online reputation management, avoiding awkwardness in social media, preventing cyber-bullying, preventing unintentional false news propagation. We observe that, such content assessment of a proposed post requires human evaluation or feedback regarding different aspects of the post in order to assist the associated user in deciding whether or not s/he should broadcast the post in social media. In this paper, we address this issue and propose a crowdsourced testbed that allows a social media user to get an evaluation of his/her proposed post before broadcasting it in actual social media, based on the feedback of specialists associated with the topics of the post. This assessment of a proposed post includes a positive/negative recommendation indicating whether or not the post should be broadcasted in social media.

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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Philosophical/theoretical foundations of artificial intelligence

      • Cognitive science

• Human-centered computing

  • Human computer interaction (HCI)