FAKE NEWS
False and often sensational information disseminated under the guise of news reporting. Or News articles that are intentionally and verifiably false and could mislead readers.
- False information: Verifiably false information
- Disinformation: False information that is intentionally created to mislead
- Misinformation: False information that spread without deliberate intent to mislead
- Mal-information: Truth shared with an intent to harm
Online Deception
- Deception: Act of hiding the truth to get an advantage.
- Deception gets people to do things they would not otherwise do.
- Factors to be considered: the deceiver, the purpose or aim of the deception, the social media service, the deception technique and the potential target.
Features used to define fake news:
- Intent – intention behind the term that is used i.e., whether the purpose is to mislead or cause harm
- Authenticity – its factual aspect i.e., whether the content is verifiably false or not.
- Knowledge – whether there is a single ground truth.
Topology – 2 major categories of fake news:
- Content based fake news: Include false texts such as hyperlinks or embedded content, multimedia such as false videos, images, audios, multimodal content (E.g., fabricated image with text related to image).
E.g.: Deep fake videos, GAN generated fake images
- Intent based fake news: Include these forms –
- Clickbait – Misleading headlines and thumbnails of content on the web that tend to be fake stories with catchy headlines aimed at enticing the reader to click on a link.
- Hoax – A false or inaccurate intentionally fabricated news story used to masquerade the truth and is presented as factual to deceive the public or audiences. E.g., stories that report the false death of celebrities.
- Rumor – Ambiguous or never confirmed claims that are disseminated with a lack of evidence to support them.
- Satire – Stories that contain a lot of irony and humor.
- Propaganda – News stories created by political entities to mislead people. E.g., online astroturfing
- Framing – Employing some aspect of reality to make content more visible, while the truth is concealed to deceive and misguide readers.
- Conspiracy Theories – The belief that an event is the result of secret plots generated by powerful conspirators.
CHALLENGES RELATED TO FAKE NEWS DETECTION AND MITIGATION
- Content Based Issue – Misleading content that resembles the truth very closely
- Contextual Issue – Inferred from the context of the online news post
- Lack of user awareness –
- Unintentional fake news spreaders are five times higher than intentional spreaders.
- Public susceptibility and lack of user awareness is the most challenging problem.
- Misinformed people are a greater problem than uninformed people because the former hold inaccurate opinions that are harder to correct.
- Social bots spreaders –
- Fake news is likely to be created and spread by non-human account.
- Bots (short for software robots) is a computer algorithm that automatically produces content and interacts with humans on social media, trying to copy and possibly alter their behaviour.
- Two models for detecting malicious accounts: Social context models achieve detection by examining features related to an account’s social presence. User behaviour models primarily focus on features related to an individual user’s behaviour.
- Another “bot-like” strategy that aims to massively promote disinformation and fake content in social platforms, called bot farms or also troll farms. It is not social bots, but it is a group of organized individuals engaging in trolling or bot-like promotion of narratives in a coordinated fashion.
- Another E.g.: Review bombing, refers to coordinated groups of people massively performing the same negative actions online in order to reduce its aggregate review score.
- Dynamic nature of OSN-
- Leads to fast propagation of fake news.
- Cause: low barriers that prevent doing so.
- Dataset Issue – There still no one size fits all benchmark dataset for fake news detection
FAKE NEWS DETECTION TECHNIQUES
- Human based techniques:
- Use human knowledge and experience to confirm the accuracy of the news reports.
- Includes crowdsourcing and fact checking techniques
- Crowdsourcing:
- Based on ‘wisdom of the crowds’.
- Crowdsource detection of emotionally manipulative language: allow crowd to detect text that uses manipulative emotional language.
- Fact-checking
- Frequently carried out by journalists by hand to confirm the accuracy of a particular assertion.
- When verifying a claim, fact-checkers must choose which data “matters the most” to clean because they are unable to clean all of it.
- AI based techniques:
- Includes the most AI approaches for fake news detection.
- Use ML, DL, NLP, DNN or combination of these.
- ML algos are designed to “learn” to act by understanding, requires human intervention to “teach them” when the result is incorrect.
- DL learn from their own mistakes, do not require human intervention.
- Blockchain Based techniques:
- Includes methods that verify the veracity of the news content’s source and establish its traceability through the use of blockchain technology.
- Solutions are still in research, beta testing stages.
- Immutability, decentralization, tamperproof, consensus, record keeping and non-repudiation of transactions are some of the key features that make blockchain technology exploitable.
Ms. KHUSHI JAIN
Assistant Professor
IT Department, JIMS VK 2