A multidisciplinary framework for deconstructing bots' pluripotency in dualistic antagonism
- URL: http://arxiv.org/abs/2402.15119v4
- Date: Sat, 11 May 2024 04:35:37 GMT
- Title: A multidisciplinary framework for deconstructing bots' pluripotency in dualistic antagonism
- Authors: Wentao Xu, Kazutoshi Sasahara, Jianxun Chu, Bin Wang, Wenlu Fan, Zhiwen Hu,
- Abstract summary: Bot-disseminated misinformation could subtly yet profoundly reshape societal processes.
We propose an interdisciplinary framework to characterise bots' emergent risks to civic discourse.
- Score: 7.152948800435588
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Anthropomorphic social bots are engineered to emulate human verbal communication and generate toxic or inflammatory content across social networking services (SNSs). Bot-disseminated misinformation could subtly yet profoundly reshape societal processes by complexly interweaving factors like repeated disinformation exposure, amplified political polarization, compromised indicators of democratic health, shifted perceptions of national identity, propagation of false social norms, and manipulation of collective memory over time. However, extrapolating bots' pluripotency across hybridized, multilingual, and heterogeneous media ecologies from isolated SNS analyses remains largely unknown, underscoring the need for a comprehensive framework to characterise bots' emergent risks to civic discourse. Here we propose an interdisciplinary framework to characterise bots' pluripotency, incorporating quantification of influence, network dynamics monitoring, and interlingual feature analysis. When applied to the geopolitical discourse around the Russo-Ukrainian conflict, results from interlanguage toxicity profiling and network analysis elucidated spatiotemporal trajectories of pro-Russian and pro-Ukrainian human and bots across hybrid SNSs. Weaponized bots predominantly inhabited X, while human primarily populated Reddit in the social media warfare. This rigorous framework promises to elucidate interlingual homogeneity and heterogeneity in bots' pluripotent behaviours, revealing synergistic human-bot mechanisms underlying regimes of information manipulation, echo chamber formation, and collective memory manifestation in algorithmically structured societies.
Related papers
- A Survey on Vision-Language-Action Models for Embodied AI [71.16123093739932]
Vision-language-action models (VLAs) have become a foundational element in robot learning.
Various methods have been proposed to enhance traits such as versatility, dexterity, and generalizability.
VLAs serve as high-level task planners capable of decomposing long-horizon tasks into executable subtasks.
arXiv Detail & Related papers (2024-05-23T01:43:54Z) - SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection [34.68635583099056]
We propose SEBot, a novel multi-view graph-based contrastive learning-enabled social bot detector.
In particular, we use structural entropy as an uncertainty metric to optimize the entire graph's structure.
And we design an encoder to enable message passing beyond the homophily assumption.
arXiv Detail & Related papers (2024-05-18T08:16:11Z) - From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora [0.12564343689544843]
Review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis.
Three key points of view are: online debates, online support, and human-AI interactions.
arXiv Detail & Related papers (2024-03-21T11:04:41Z) - Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators [2.500481442438427]
We analyse speech generation incidents to study how patterns of specific harms arise.
We propose a conceptual framework for modelling pathways to ethical and safety harms of AI.
Our relational approach captures the complexity of risks and harms in sociotechnical AI systems.
arXiv Detail & Related papers (2024-01-25T11:47:06Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - Speech-Gesture GAN: Gesture Generation for Robots and Embodied Agents [5.244401764969407]
Embodied agents, in the form of virtual agents or social robots, are rapidly becoming more widespread.
We propose a novel framework that can generate sequences of joint angles from the speech text and speech audio utterances.
arXiv Detail & Related papers (2023-09-17T18:46:25Z) - Stable Bias: Analyzing Societal Representations in Diffusion Models [72.27121528451528]
We propose a new method for exploring the social biases in Text-to-Image (TTI) systems.
Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts.
We leverage this method to analyze images generated by 3 popular TTI systems and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents.
arXiv Detail & Related papers (2023-03-20T19:32:49Z) - Countering Malicious Content Moderation Evasion in Online Social
Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems.
This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
arXiv Detail & Related papers (2022-12-27T16:08:49Z) - Political Propagation of Social Botnets: Policy Consequences [0.0]
The 2016 US election was a watershed event where an electoral intervention by an adversarial state made extensive use of software robots and data driven communications.
We reflect upon the policy consequences of the use of Social Botnets and understand the impact of their adversarial operation.
For future work, it is important to understand the agency and collective properties of these software robots.
arXiv Detail & Related papers (2022-05-10T12:08:03Z) - Data-driven emotional body language generation for social robotics [58.88028813371423]
In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration.
We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions.
The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
arXiv Detail & Related papers (2022-05-02T09:21:39Z) - Mechanisms for Handling Nested Dependencies in Neural-Network Language
Models and Humans [75.15855405318855]
We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing.
Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement.
We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
arXiv Detail & Related papers (2020-06-19T12:00:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.