In today’s world disinformation is spread more easily than ever before with the advent of social media. Humans, by nature, are vulnerable to general misinformation if that misinformation is being presented to them in their own information bubble (Menczer 2016). Social media like Facebook and Twitter use algorithms to determine which posts and articles we see and maybe, and more importantly, what we do not. These algorithms prioritize these possible posts on your feed on their expected level of impact on the user, such as engagement level, how likely we are to share, respond and share the post (Menczer 2016).
There are some disinformation campaigns that have been widely talked about in the news. The most notorious one would be the disinformation campaign from ‘russian trolls’ in the 2016 U.S. presidential elections. Where these so called trolls brigaded social media such as Facebook and Reddit and wrote articles and blogs to try to polarize and further divide the two political parties.
So how can we combat disinformation campaigns and the spread of misinformation? The first possible solution is better information sharing between governments and tech companies in order to be more transparent and join forces in this matter. A more interesting and direct approach is the use of deep neural networks for fake news detection. Contrary to current methods of detecting misinformation like automatic fact-checking or reaction based analysis, deep neural networks like 3HAN try to interpret the structure of a text. It analyzes each word, sentence, body and headline of a piece of text. This method has an accuracy of 96.77% in detecting fake news articles (Singhania et al. 2018).
Technology plays a big part in the spread of misinformation, but it can also solve it. However, how much will it differ if top ranking official and even the president of the United States spread misinformation daily to further their agenda.
Bibliography:
Menczer, F. (2018). Misinformation on social media: Can technology save us?. [online] The Conversation. Available at: https://theconversation.com/misinformation-on-social-media-can-technology-save-us-69264 [Accessed 30 Sep. 2018].
Singhania, S., Fernandez, N. and Rao, S. (2017). 3HAN: A Deep Neural Network for Fake News Detection. ICONIP.
