Social Media is a place where a massive data is generated in seconds and it’s the first place for any news to land. Having said that, there is huge chance to spread Fake news and it’s one the biggest challenging issue in social media. Following are some of the high level concepts in Fake new detection.
Characterization and Detection are the two aspects of fake news detection.
What is Characterization ?
This phase talks about fake news and its characteristics. Classified into the following two categories Traditional and Social Media.
Traditional Media ( Psychology and Social Foundation )
| Naıve Realism | Consumers believe their perceptions are correct |
| Confirmation Bias | Consumers prefer to receive information that confirms to their views |
Social Media ( Malicious Accounts and Echo Chambers )
| Malicious Accounts | Bots ,Cyborg users, Trolls. |
| Echo Chambers | Groups of like minded people spreading the news |
What is Detection ?
This Phase talks about the fake news detection methodologies. Detection Algorithms are highly classified into two categories Content and Social context based.
| Knowledge Based | Used External Sources for checking the credibility of the news. |
| Style Based | Relies on the same source but manipulates basing on the writing style. |
| Stance Based | Uses user viewpoint |
| Propagation Based | Checks for the relevance in social media posts. |
To train the model We can get data from different sources . However checking veracity of the news is challenging . Therefore following are some of the publicly available data sets
| BuzzFeedNews | This dataset comprises a complete sample of news published in Facebook from 9 news agencies over a week close to the 2016 U.S. election from September 19 to 23 and September 26 and 27. Every post and the linked article were fact-checked |
| LIAR | This dataset is collected from fact-checking website PolitiFact through its API |
| BS Detector | This dataset is collected from a browser extension called BS detector developed for checking news veracity |
| CREDBANK | This is a large scale crowdsourced dataset of approximately 60 million tweets that cover 96 days starting from October 2015 |
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Fake News Related Areas
| Rumor Classification | Aims to classify piece of information as rumor or not and its has different phases as Rumor detection, rumor tracking, stance classification, and veracity classification |
| Truth Discovery | Detecting truth from multiple conflicting sources. |
| Clickbait Detection | Detecting eye catching teaser in online Media |
| Spammer and Bot Detection | Detecting malicious users , Spreading ads, Viruses and Phishing etc. |
Future Research
Fake news detection is one of the emerging research areas . Its broadly outlined into four different areas like Data-oriented, Feature-oriented, Model-oriented, and Application-oriented.
| Data-oriented | The main focus here is on data such as benchmark data collection, Fake news validation etc |
| Feature-oriented | Focus here is for detecting fake news from multiple data sources, such as news content and social context. |
| Model-oriented | Focuses more on practical and effective models for fake news detection, including supervised, semi-supervised and unsupervised models |
| Application-oriented | This research goes beyond fake news detection, such as fake new diffusion and intervention |
Reference: https://www.kdd.org/exploration_files/19-1-Article2.pdf
Data Shastra 