Neural Information Processing Systems http://nips.cc/

Social media has created new ways for citizens to stay informed on societal matters and participate in political discourse. However, with its algorithmically-curated and virally-propagating content, social media has contributed further to the polarization of opinions by reinforcing users' existing viewpoints. An emerging line of research seeks to understand how content-recommendation algorithms can be re-designed to mitigate societal polarization amplified by social-media interactions. In this paper, we study the problem of allocating seed users to opposing campaigns: by drawing on the equal-time rule of political campaigning on traditional media, our goal is to allocate seed users to campaigners with the aim to maximize the expected number of users who are co-exposed to both campaigns. We show that the problem of maximizing co-exposure is NP-hard and its objective function is neither submodular nor supermodular. However, by exploiting a connection to a submodular function that acts as a lower bound to the objective, we are able to devise a greedy algorithm with provable approximation guarantee. We further provide a scalable instantiation of our approximation algorithm by introducing a novel extension to the notion of random reverse-reachable sets for efficiently estimating the expected co-exposure. We experimentally demonstrate the quality of our proposal on real-world social networks.

Neural Information Processing Systems http://nips.cc/

Social media platforms serve as a significant medium for sharing personal emotions, daily activities, and various life events, ensuring individuals stay informed about the latest developments. From the initiation of an account, users progressively expand their circle of friends or followers, engaging actively by posting, commenting, and sharing content. Over time, user behavior on these platforms evolves, influenced by demographic attributes and the networks they form. In this study, we present a novel approach that leverages open-source models Llama-3-Instruct, Mistral-7B-Instruct, Gemma-7B-IT through prompt engineering, combined with GPT-2, BERT, and RoBERTa using a joint embedding technique, to analyze and predict the evolution of user behavior on social media over their lifetime. Our experiments demonstrate the potential of these models to forecast future stages of a user's social evolution, including network changes, future connections, and shifts in user activities. Experimental results highlight the effectiveness of our approach, with GPT-2 achieving the lowest perplexity (8.21) in a Cross-modal configuration, outperforming RoBERTa (9.11) and BERT, and underscoring the importance of leveraging Cross-modal configurations for superior performance. This approach addresses critical challenges in social media, such as friend recommendations and activity predictions, offering insights into the trajectory of user behavior. By anticipating future interactions and activities, this research aims to provide early warnings about potential negative outcomes, enabling users to make informed decisions and mitigate risks in the long term.

Summary and Contributions: The paper considers an extension to the influence maximization problem where two campaigns co-exist in the network and the objective is to allocate a set of seed-nodes to each campaign, under cardinality constraints, such that the expected number of nodes that are exposed to both campaigns is maximized. The diffusion model is assumed to be the Independent Cascade model. The problem is well-motivated, as it is clear that the problem of polarization exists in social networks and media, and the network owners have incentives to take action in reducing the polarization among their users. The theoretical findings in the paper are very interesting and novel, and the ideas explored can be applicable to other problems as well. I enjoyed reading the paper.

Social media has created new ways for citizens to stay informed on societal matters and participate in political discourse. However, with its algorithmically-curated and virally-propagating content, social media has contributed further to the polarization of opinions by reinforcing users' existing viewpoints. An emerging line of research seeks to understand how content-recommendation algorithms can be re-designed to mitigate societal polarization amplified by social-media interactions. In this paper, we study the problem of allocating seed users to opposing campaigns: by drawing on the equal-time rule of political campaigning on traditional media, our goal is to allocate seed users to campaigners with the aim to maximize the expected number of users who are co-exposed to both campaigns. We show that the problem of maximizing co-exposure is NP-hard and its objective function is neither submodular nor supermodular.

Wildlife trafficking (WLT) has emerged as a global issue, with traffickers expanding their operations from offline to online platforms, utilizing e-commerce websites and social networks to enhance their illicit trade. This paper addresses the challenge of detecting and recognizing wildlife product sales promotion behaviors in online social networks, a crucial aspect in combating these environmentally harmful activities. To counter these environmentally damaging illegal operations, in this research, we focus on wildlife product sales promotion behaviors in online social networks. Specifically, 1) A scalable dataset related to wildlife product trading is collected using a network-based approach. This dataset is labeled through a human-in-the-loop machine learning process, distinguishing positive class samples containing wildlife product selling posts and hard-negatives representing normal posts misclassified as potential WLT posts, subsequently corrected by human annotators. 2) We benchmark the machine learning results on the proposed dataset and build a practical framework that automatically identifies suspicious wildlife selling posts and accounts, sufficiently leveraging the multi-modal nature of online social networks. 3) This research delves into an in-depth analysis of trading posts, shedding light on the systematic and organized selling behaviors prevalent in the current landscape. We provide detailed insights into the nature of these behaviors, contributing valuable information for understanding and countering illegal wildlife product trading.

Short-form video (SFV) has become a globally popular form of entertainment in recent years, appearing on major social media platforms. However, current research indicate that short video addiction can lead to numerous negative effects on both physical and psychological health, such as decreased attention span and reduced motivation to learn. Additionally, Short-form Video Addiction (SFVA) has been linked to other issues such as a lack of psychological support in real life, family or academic pressure, and social anxiety. Currently, the detection of SFVA typically occurs only after users experience negative consequences. Therefore, we aim to construct a short video addiction dataset based on social network behavior and design an early detection framework for SFVA. Previous mental health detection research on online social media has mostly focused on detecting depression and suicidal tendency. In this study, we propose the first early detection framework for SFVA EarlySD. We first introduce large language models (LLMs) to address the common issues of sparsity and missing data in graph datasets. Meanwhile, we categorize social network behavior data into different modalities and design a heterogeneous social network structure as the primary basis for detecting SFVA. We conduct a series of quantitative analysis on short video addicts using our self-constructed dataset, and perform extensive experiments to validate the effectiveness of our method EarlySD, using social data and heterogeneous social graphs in the detection of short video addiction.

Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.

Examples of bias and discrimination in AI applications include court decisions [1], job hiring [2], online ads [3], and many other areas prone to bias [4]. These algorithmic decisions have economic and personal implications for individuals. Therefore, Fairness, Accountability, Transparency and Ethics (FATE) in AI must be properly regulated for responsible use cases [5, 6], particularly in high-stakes domains [1, 7, 8, 9, 10, 11, 12]. Studies have shown that machine learning models can discriminate based on race and gender [13, 14, 15]. FATE in AI is intended to address the social issues caused by digital systems, but the current discourse is largely shaped by more economically developed countries (MEDC), raising concerns about neglecting local knowledge, cultural pluralism, and global fairness [16]. As AI systems become more integrated into various products [9, 10, 17, 12, 18, 19], they are a major driver of the fourth industrial revolution (4IR) and transformation [20]. Therefore, it is essential to understand the FATE-related needs of different communities, as AI affects a wide range of people. Ensuring effective transparency cannot be a one-size-fits-all approach [21], as this could disproportionately affect different communities [16, 22]. To this end, more contextualised and interdisciplinary research is needed to inform algorithmic fairness and transparency [23, 24, 25].