Covid-19 relaterade konspirationsteorier på sociala medier

Distinguishing between information and disinformation is an ever growing issue. The dramatic structure of a conspiracy theory easily captures a large audience and with the advent of social media, this disinformation can spread at an ever growing rate. This is especially true with the infodemic following the Covid-19 pandemic in early 2020, where there was a drastic increase in Covid-19 related misinformation on social media. When misinformation replaces fact, some people will inevitably follow borderline dangerous advice. This could unfortunately be seen in the ivermection issue where people injected this substance in hope of preventing/curing a Covid-19 infection. This is why finding patterns in disinformation that distinguishes it from facts would allow us to take measures against the spread of conspiracy theories. We have found patterns in our dataset suggesting that there is a significant difference in the language patterns for terms relating to conspiracy theories, and non-conspiratorial terms. We find that the sentiment of conspiracy theories is very volatile when compared to that of non-conspiratorial terms which follow a more neutral pattern in terms of sentiment. Suggesting that the language usage in a post can be used as a factor when determining the credibility of its content. We also find that conspiracy theories tend to see a drastic increase in mentions when previously being relatively lowin mentions. The result of this thesis could therefore be used as a start for developing tools and processes which would seek to combat the spread of conspiracy theories and limit the potential harm that could come from them