![]() Social media companies have been developing the ability to automatically sort tweets and social media messages by sentiment, using machine learning to identify patterns associated with positive or negative tweets. Several tactics contributed to the success of these efforts, such as feeding fake content to real users for distribution by monitoring who shared news stories, and what they tended to share. The careful study of online conversations used by activists or other politically involved users was a vital part of these campaigns. Often, these talks would appear to be from a valid or known source, or at the very least, a member of an existing fringe group. Once sensitive topics were gathered, the fake news was aggressively injected into the public discussion by strategically placed bots targeting real users determined likely to share the content as legitimate. To enable these campaigns, online conversations were carefully studied to craft information that would have the maximum impact. These attacks used the anonymous nature of Twitter and other social platforms to attempt to deepen political and social rifts in US society by spreading false and misleading information designed to be polarizing. Disinformation campaigns were made famous by the tactics discovered to be in use during the 2016 election tampering operations used by Russian-linked hackers. ![]()
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