Fake News Detection with Generated Comments For News Articles
ABSTARCT :
Recently, fake news is shared via social networks and makes wrong rumors more diffusible. This problem is serious because the wrong rumor sometimes makes social damage by deceived people. Fact-checking is a solution to measure the credibility of news articles. However the process usually takes a long time and it is hard to make it before their diffusion. Automatic detection of fake news is a popular researching topic. It is confirmed that considering not only articles but also social contexts(i.e. likes, retweets, replies, comments) supports to spot fake news correctly. However, the social contexts are naturally unavailable when an article comes out, making early fake news detection by means of the social context useless. We propose a fake news detector with the ability to generate fake social contexts, aiming to detect fake news in the early stage of its diffusion where few social contexts are available. The fake context generation is based on a fake news generator model. This model is trained to generate comments using a dataset which consists of news articles and their social contexts. In addition, we also trained a classify model. This used news articles, real-posted comments, and generated comments. To measure our detector s effectiveness, we examined the performance of the generated comments for articles with real comments and generated ones by the classifying model. As a result, we conclude that considering a generated comment help detect more fake news than considering real comments only. It suggests that our proposed detector will be effective to spot fake news on social networks.
EXISTING SYSTEM :
Fake news on social media is a major challenge and studies have shown that fake news can propagate exponentially quickly in early stages. Therefore, we focus on early detection of fake news, and consider that only news article text is available at the time of detection, since additional information such as user responses and propagation patterns can be obtained only after the news spreads. However, we find historical user responses to previous articles are available and can be treated as soft semantic labels, that enrich the binary label of an article, by providing insights into why the article must be labeled as fake.
DISADVANTAGE :
? We have an ongoing project to develop a us-able dataset for fake news detection on social media. This dataset, called Fake New set20, includes all mentioned news content and social context features with reliable ground truth fake news labels.
? The detection task can be defined as: given a news article dither target is to predict the corresponding label fi. However, it is important to understand that user response x corresponding to each article d is available in historical training data during the training process.
PROPOSED SYSTEM :
We propose a novel Two-Level Convolution Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection. We conduct experiments on one available dataset and a larger dataset collected by ourselves. Experimental results show that TCNN-URG outperforms the baselines based on prior approaches that detect fake news from article text alone.
ADVANTAGE :
? However, because its cheap to provide news online and much faster and easier to disseminate through social media, large volumes of fakeness, i.e., those news articles with intentionally false information, are produced online for a variety of purposes, such as financial and political gain.
? It was estimated that over 1million tweets are related to fake news “Pizzagate”3by the end of the presidential election. Given the prevalence of this new phenomenon, “Fake news” was even named the word of the year by the Macquarie dictionary in 2016
|