Sentiment Analysis of Misinformation & Fake News

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  Distribution of Sentiment Category in fake news

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  Distribution of Sentiment Category in true news

Inspiration

Fake news is a growing problem in today's world, where misinformation can spread rapidly and have serious consequences. From politics to public health, fake news can distort our understanding of important issues and undermine the foundations of democracy.

It's up to each of us to be vigilant and discerning in our consumption and sharing of news, and to take responsibility for verifying the accuracy of the information we encounter.

Approach

When human readers approach a text, we use our understanding of the emotional intent of words to infer whether a section of text is positive or negative, or perhaps characterized by some other more nuanced emotion like surprise or disgust.

One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.

AFINN from Finn Årup Nielsen is based on unigrams(or single words). This lexicon contain many English words and the words are assigned with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.

For fake news, we put hypothesis that there is a greater negative sentiment being spread over positive sentiments which will eventually led to spread a negative impression and misinterpretation .

Challenges

Data Source - The news data is about electoral campaign in America. Hence, a massive highlight of prominent political parties, political parties and agenda is throughout the data.

Computational Power - The analysis is limited due to the massive size and requirement of large computational algorithm

Accomplishments that we're proud of

Fake News have higher negative sentiment over positive sentiment than true news. Hence, it is essential to always understand the underlying tone of news spread and verify the details accordingly

What's next for Sentiment Analysis of Misinformation & Fake News

With increasing development in open source algorithm ,we will essentially soon have applications in mobile devices to verify the news

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