How Artificial Intelligence Can Be Used to Fight Hate Speech Online

Hate speech is generally characterized as the promotion of violence against a person or group because of gender, race, or religion. Hate speech fanatics typically harbor a false sense of self-righteousness and toxic superiority complex. 

Supporters of the ideology (whatever their prejudice may be) flood social media platforms, online forums, and discussion groups with unsettling messages, pieces of media, and more. Subtle prejudice can be seen through frequent and massive trolling, manufacturing of conspiracy theories, and meme-related media. 

Hate speech on the internet can have extreme consequences. Words hurt, and unsettling threats issued over the internet generally have large impacts on victims. Whether it be an anti-Semitic “meme,” or a profile picture depicting a burning pride flag, hate speech has a large prominence on the world wide web. 

Technology giants such as Facebook, Twitter, and Google have started initiatives to fight hate speech on their platforms, using automated and manual verbal filters. However, due to the enormous number of users the platforms have, the task of incorporating a fully-fledged autonomous ML-platform with self-supervisory learning capabilities and filtering can be extremely daunting.

Some corporations are still relying on pseudo Machine learning deployments to detect and analyze verbal wording of billions of users’ post texts, images, and videos posted on their websites. On the other hand, hate speech fanatics have found ways to circumvent and bypass these verbal filtering manipulations by adding non-word characters on sentimental keywords that are subject to anti-hate tracking. 

There is no doubt Artificial Intelligence has the potential to take down hate speech online but building a sophisticated system with instantaneous capabilities to analyze billions of characters on the platform requires considerations factors such as: 

Structural policy adoption and willpower  

While it isn’t directly related to the hardcore workload, willpower specifies an unwavering willingness and sense of responsibility for platform owners to invest and implement technology-driven strategies that curtail hate speech on their sites. Many Big Tech officials are not doing enough to eliminate hate speech online. In particular, Facebook continues to be criticized for showing limited willingness to deal with racist and hateful content on its platform. The first step to solving the problem is recognizing it, and Big Tech executives need to be willing to acknowledge the issue. 

Building sufficient Datasets to facilitate ML/AI Data Analysis and implement auto- filtering operations

Designed algorithms must be able to: 

• Implement sentimental analysis at an accuracy rate of 90%.
• Perform dimensional keyword tracking based on a constructed dataset. 
• Intelligently carry out necessary filters with limited or minimal bias rate. 

The successful deployment and implementation of Artificial Intelligence to fight online hate relies on one big secret. Building a massive data-harvest engine enables you to create large datasets with an enormous library of billions of keywords. It’s used to run automatic analysis, and define verbal wordings and phrases perceived to contain hateful tones or connotations. 

AI relies on the availability of enough data to make meaningful decisions. Without data, it is impractical.

The best way to solve the date problem is by building an automatic data-harvest engine, possibly using deep learning algorithms. It would be impossible for humans to use manual data collection methods to develop large datasets that feed systems like Facebook’s or Google’s anti-hate speech computation engine. 

Unfortunately, hate speech fanatics are generally evasive through their techniques of bypassing the set filters using string-character integration. An ML algorithm must be smart enough to keep building its own datasets in the database and run continuous tracking of suspicious words, based on specific manually generated rules by Admins or based on standards auto-defined by the system itself using certain detected behaviors of the users on the platform. 

Multi-lingual verbal wording analysis and filtering could also pose a severe challenge. Although companies like Facebook, Twitter, or Google have multi-lingual integration implemented with their applications, not all world languages are incorporated. This means that hate speech enthusiasts generally take advantage of undefined languages. However, successful sophistication of Natural Language Processing (NLP) integration into the system can give any engine the ability to translate and analyze any text.

Ensuring Performance acceleration (from a hardware, software, and network standpoint)

Artificial Intelligence operations are crazily resource-hogging in terms of memory, computation (processing), and data transfer (bandwidth). This is why many enterprises are still struggling to integrate these technologies into their mainstream technology infrastructure.  Leveraging permeance acceleration technologies on the hardware level and robust compression on the network data transfer level can alleviate some of the worries.

However, given the amount of progress that we have made in the past few years, Artificial Intelligence will likely be advanced enough to keep the platforms clean.