Information influences the way humans perceive their environment, interact with others, and control their decision-making. Due to the popularity of social media, it has become impossible to determine a credible source of information. For example, when Covid-19 hit, several theories circulated stating rumors: hot water can prevent Covid-19 or masks are not needed, or Covid-19 is a conspiracy to curb our rights. Citizens ignored Lockdowns due to circulations of false messages, causing the further spread of covid, which eventually led to the tragic loss of life.
This is where our digital warrior comes to save the day. Natural Language Processing (NLP) helps Artificial Intelligence (AI) determine what sources can be trusted (learn the difference between AI and NLP). To control the spread of misinformation, we can utilize natural process language to interpret information correctly.
A survey conducted in 2020 concluded that most of the population was taking Facebook and Instagram posts as facts. It did not matter who was posting the content; social media users would assume it to be valid if it was on their feed. This creates chaos and affects the decisions of every individual.
NLP has an armory of weapons like stance detection, abstractive information, fact-checking, and sentiment analysis, to name a few that help AI determines a credible source of information.
Stance detection can be defined as a tool that helps determine whether the writer favors or is against the claim he has released. It aims to test the consistency between the claim rather than the accuracy of the information, which may help search for evidence and remove sources for obtaining false information.
For example: If a person tweets, "Apples are my favorite fruit." Then, stance detection will recognize that the writer's stance is in favor of apples. Similarly, if they tweet, "Apples make my teeth hurt." Then, stance detection will determine that the writer is against apples. Lastly, if they tweet, "Apples aren't delicious, but I do not mind them." Then the system will determine that the writer is neutral about apples.
Stance detection digs deep into the source text. It is capable of determining multiple claims on one source. There may be more than one stance in one article.
Abstractive summarization is a tool in natural language processing (NLP) that presents a clear outline of the source text given. Abstractive summarization doesn't merely copy necessary phrases from the source text but can also generate new relevant words, perceived as paraphrasing. The system has to process a lot of data; abstractive summarization strips the data to its essentials, removing unnecessary information to process, making it easier to remove false information.
Fact-checking is an NLP tool used to check the veracity of a source text or document. NLP uses N-grams(An N-gram means a sequence of N words) to determine what kind of statistical methods are used and the pairings of n words. The most common type of pairings that are used is Bigrams and Trigrams. A bigram: an n-gram is 2 (n=2). For instance, 'small blog' is a bigram. Correspondingly, a trigram: an n-gram is 3 (n=3). For example, 'A small blog' is a trigram. So, entire sentences and parsed sentences can be grouped. There is no clear distinction between information detection and fact-checking since both aim to assess the honesty of claims, although info detection sometimes focuses on a particular section while fact-checking is broader.
Sentiment analysis means understanding the emotion behind a statement. It is a tool of NLP that helps AI (Artificial Intelligence) understand the human feeling behind a document or message. When we humans communicate, we use 'natural language' and can easily understand the intent or emotion behind a statement, but it is pretty messy and challenging for a machine to understand. In these scenarios, sentiment analysis comes into the picture. A recent example of how sentiment analysis improved a social media platform: A few years back, Facebook only had the option to either like or not engage with a post. Now, we have many options like love, like, angry, sadness, excitement, etc. YouTube has also used sentiment analysis to combat hateful comments on their platform.
With so many sources of information NLP helps AI battle misinformation through tools like stance detection, Abstractive summarization, fact-checking, sentiment analysis, and many more. Each tool allows the AI to learn something about the source text, whether it understands the point of view, the emotion, or the stance behind it. This information ultimately helps the AI fact check and determine whether the information given is true or false. Of course, it is not a perfect system, but the more information a team provides their AI, the more it learns and perfects itself. Unlike humans, the wonderful thing about AI is that it never makes the same mistake: Once it knows something, it continually improves its efficiency, which makes it a marvelous assistant for us humans.
"Most comprehensive labelling tool in the market. Datasaur has saved us countless hours in building our own solution. My team lead never wants to go back to spreadsheets!"
"Operating in an industry where we have to be privacy- and security-conscientious with our data, Datasaur was the only acceptable solution for us. We recommend them for both feature set and support responsiveness."
"...information labeling tasks has been reduced by 80% which has allowed us to optimize our workflow much more, allowing us to focus on other areas that are also priorities for us..."