Computational Propaganda: Evolving Techniques, Challenges, and the Need for Revised Propaganda Theory

Core Concepts

Computational propaganda, the use of algorithms, automation, and human curation to spread misleading information on social media, has become a significant threat to democracy. Classical propaganda theory needs to be revised to address the new modalities of propaganda in the digital age. Bot detection systems face challenges in identifying sophisticated, coordinated bot activities, and require advancements to address the limitations of current approaches.

Abstract

The content discusses the evolution of propaganda in the digital age, known as computational propaganda. It outlines how the rise of the internet and social media has changed the way propaganda is carried out, with the use of automation, bots, and human curation to distribute misleading information and manipulate public opinion.

The article highlights the key differences between classical propaganda and computational propaganda, such as the decentralized mode of content proliferation and potential anonymity provided by social media. It argues that the classical propaganda theory needs to be revised and redefined to suit the present context.

The content also delves into the various machine learning frameworks developed for bot detection, including supervised and unsupervised approaches. It discusses the limitations of these systems, such as their lack of scalability, generalizability, and the inability to detect coordinated bot activities. The article emphasizes the need for more advanced bot detection systems that can handle the evolving sophistication of bots, including the use of AI techniques to generate credible content and the formation of botnets.

Furthermore, the content highlights the challenges faced by bot detection systems due to the limited data provided by social media platforms through their API services. It suggests the need for collaboration between social media platforms and academic researchers to better understand the effects of computational propaganda on public opinion and behavior.

The article concludes by emphasizing the importance of revising the conceptual and epistemological frameworks in propaganda studies to address the new modalities of propaganda in the digital age, and the need for further research and advancements in bot detection systems to curb the manipulation and effects of social bots.

Stats

Bots have been actively involved in online discussions of important events, including US elections in 2016 and 2020 as well as in Brexit.
Bots have been deployed by powerful elites during political elections to demobilize an opposing party's followers, to target cyber-security threat or political-cultural threats from other states, to attack in-state targets and to send out pro-government or pro-candidate microblog messages.
According to an experimental research among 656 people, participants were able to identify bot accounts with just 71% accuracy among 20 profiles, and conservative bots were more likely to be misidentified than liberal bots.

Quotes

"Computational propaganda is the use of algorithms, automation and human curation to purposefully distribute misleading information over social media networks to manipulate public opinion, for political polarization etc."
"Digital media has blurred the line between vertical and horizontal propaganda as individual groups and powerful organizations can all potentially create and orchestrate disguised campaigns within the same online environment."
"The main challenge for bot detection systems is the limited amount of data that social media platforms provide through their API service, which questions the generalizability of the system."

Key Insights Distilled From

Computational Propaganda Theory and Bot Detection System

by Manita Pote at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05240.pdf

Deeper Inquiries

How can the collaboration between social media platforms and academic researchers be improved to enable a better understanding of the effects of computational propaganda on public opinion and behavior?

Collaboration between social media platforms and academic researchers can be enhanced by establishing transparent and structured partnerships. Social media platforms should provide researchers with more comprehensive and diverse datasets that include not only user account information but also emotional nature of misinformation, exposure to evocative content, and engagement with misinformation. This will enable researchers to analyze the impact of computational propaganda on public opinion and behavior more effectively. Additionally, platforms can create dedicated research portals or APIs that offer real-time access to data for academic studies. By fostering open communication and cooperation, both parties can work together to address the challenges posed by computational propaganda.

What are the potential ethical and legal implications of the use of AI techniques by bots to generate credible content and evade detection?

The use of AI techniques by bots to generate credible content and evade detection raises significant ethical and legal concerns. From an ethical standpoint, the creation of sophisticated bots that can mimic human behavior blurs the line between real and fake information, leading to misinformation and manipulation of public opinion. This can undermine trust in online content and democratic processes. Moreover, the deployment of AI-powered bots without transparency or disclosure violates principles of honesty and integrity in communication.
Legally, the use of AI by bots to deceive users may contravene regulations related to consumer protection, data privacy, and intellectual property rights. For instance, if bots generate content that infringes on copyrights or trademarks, legal action can be taken against the bot operators. Additionally, the dissemination of false information through AI-generated content could lead to defamation or libel claims. It is crucial for policymakers to address these ethical and legal implications by implementing regulations that govern the use of AI in online communication and ensuring accountability for deceptive practices.

How can bot detection systems be designed to be more platform-independent and adaptable to changes in social media data structures?

To enhance the platform independence and adaptability of bot detection systems, researchers can explore innovative approaches that focus on broader features beyond platform-specific data structures. One strategy is to develop bot detection algorithms that rely on universal characteristics of bot behavior, such as posting frequency, content similarity, and engagement patterns, rather than platform-specific attributes. By utilizing these generic features, bot detection systems can be more versatile and applicable across various social media platforms.
Furthermore, researchers can leverage advanced machine learning techniques, such as transfer learning and ensemble methods, to create models that can adapt to changes in social media data structures. Transfer learning allows bot detection systems to leverage knowledge from one platform to another, while ensemble methods combine multiple models to improve detection accuracy and robustness. By incorporating these techniques, bot detection systems can become more resilient to changes in data structures and better equipped to identify evolving bot behaviors across different platforms.