The sheer amount of content posted every day on social media platforms is staggering – every minute, millions of images, videos, and messages are shared across various social media networks, which makes manually analyzing this data an impossible task even for the largest of corporations.
Given the massive amounts of information that needs to be processed, it’s clear that employing millions of people to do this grueling task isn’t realistically possible – and that’s where AI steps in.
How AI Technology Tackles Disinformation
AI technology is capable of detecting false or misleading information on social media websites with incredible accuracy by utilizing a multitude of advanced techniques and learning models – one of them being Natural Language Processing (NLP). In essence, NLP is a branch of AI that enables machines to read and fully understand how we communicate.
In the context of fake news detection, NLP looks for patterns and linguistic cues that may indicate unreliable or misleading information by breaking down text into smaller components like words, phrases, and sentences. It then identifies unusual word choices, grammatical structures, or writing styles that could be associated with disinformation or fake news.
Once the linguistic cues are examined, the next technique NLP models use is sentiment analysis, which allows AI to determine if the emotional tone of the message is positive, negative, or neutral. This approach helps identify content designed to provoke strong reactions – a common tactic often used by bad actors when spreading disinformation.
Threat actors, also known as bad actors, are individuals or groups whose intention is to cause harm to companies, brands, and the general public through means of disinformation.
They usually do this by targeting people’s biases and fueling their emotions, knowing that the more controversial the topic, the likelier it is for people to invest themselves into it and amplify the spread of false information. Sentiment analysis combats this by detecting extreme language, exaggerated claims, or attempts to manipulate readers’ emotions.
Named entity recognition (also known as entity chunking, and entity extraction) is another NLP technique that helps in fact-checking by identifying and categorizing people, places, organizations, and other entities mentioned in posts – allowing AI systems to cross-reference these entities with reliable databases, flagging inconsistencies or false claims.
For example, if a viral post claims Nike has been exposed for using child labor in a new Indonesian factory, the NLP system would recognize “Nike” and “child labor” as two important factors in this sentence.
It would then cross-reference this information with verified news sources and official company statements, and if no credible sources corroborate this claim, the system would flag this as potential disinformation.
The Battle Against Bot Manipulation
Threat actors, who we’ve previously mentioned in the article, often deploy bots to start or latch on to controversial topics in order to deceive the general public.
In the past, creating a bot account was a tedious task that often required manual labor, but the rise of GenAI has allowed bad actors to create thousands of bots in a matter of minutes.
To make matters worse, these are not your typical easy-to-distinguish bots – on the contrary, they are accounts with authentic-looking profile pictures and detailed biographies that make them appear convincingly real.
By now, most people are aware of the massive impact bot networks could have on the real world – from elections to public opinion on pressing issues, their influence cannot be overstated, which only further demonstrates the critical need for tools that can halt their impact.
AI tools can combat their efforts by using sophisticated behavior pattern analysis. This means AI models can thoroughly analyze various aspects of bot account’s activity, including posting frequency, timing patterns, and content similarity across posts to identify patterns that are uncharacteristic of human behavior.
Advanced AI tools take this a step further by detecting coordinated posting activities across multiple accounts, which is particularly effective in uncovering networks of bots working in tandem to amplify certain messages or manipulate trends – such as stock market manipulation.
Cyabra: The All-in-One AI Social Media Monitoring Tool
Cyabra’s AI platform is a state-of-the-art social media monitoring tool that allows businesses, companies, and brands to easily keep track of all brand mentions and online discussions, understand narratives and follow their spread.
Additionally, Cyabra’s solution is extremely reliable at detecting coordinated inauthentic profile connections across social media platforms, fake accounts detection, and uncovering bot networks that could potentially harm your brand’s reputation. It can also detect harmful GenAI content.
Cyabra can analyze vast amounts of data in real-time, safeguarding your digital assets and providing actionable insights that go beyond surface-level metrics, which allows your organization to not only monitor your online presence, but to also proactively address potential threats and dis- or misinformation campaigns before they escalate.
