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[D] Classification of Misinformation and Scaled Sentiment Analysis

I do not know if this title best captures my idea for this post but I will nonetheless continue. Recently, I have been concerned with the extent of misinformation present in the media in the form of unsubstantiated claims and conspiracies as well as deepfakes and other similar things. This, along with human biases and unconscious judgements towards certain believes or topics, creates a climate where a tremendous amount of attention and force can be maliciously and wrongly directed towards some person or other entity and there is no “truth” or authority to set things right or to educate people correctly. My topic for discussion here, given this opening, is what measures can be taken, employing different ML techniques, especially those for sentiment analysis, to prevent digital mobs from unjustly releasing their wrath and to undo the effects of users having been exposed to unsubstantiated and potentially dangerous false information? What would you enact if you worked at some the company of some web browser or social media company to ensure that misinformation didn’t dominate conversation or proliferate further? Some topics I have in mind that I have yet to examine deeply are YouTube’s algorithm’s progressive recommendations of more radical content or Instagram failing to qwell networks that propogate anti-vaccination and other conspiracy rhetoric. There seems to me to be an erosion of evidenced information and a reversion to a chaotic environment where people are guided by the simplest, most tribalist claims. What are your thoughts?

submitted by /u/unsupervisedmodeler
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.