Evaluating the Generalization Capabilities of Community Models for Malicious Content Detection on Social Media

Conceitos essenciais

Community models for malicious content detection on social media graphs often perform well on benchmark datasets but struggle to generalize to new graphs, domains, and tasks. A novel few-shot subgraph sampling approach is proposed to better assess inductive generalization capabilities of these models.

Resumo

The paper highlights the limitations of current evaluation setups for community models on malicious content detection, which neglect the rapid evolution of online content and the underlying social graph. It proposes a novel evaluation setup based on a few-shot subgraph sampling approach to test for inductive generalization.
The key points are:

Community models for malicious content detection, which leverage the social graph context, have shown strong performance on benchmark datasets. However, misinformation and hate speech continue to propagate on social media.
This mismatch can be attributed to the limitations of current evaluation setups that do not account for the dynamic nature of online content and communities.
The proposed few-shot subgraph sampling approach generates local, limited-context subgraphs with few labeled examples, emulating more realistic application settings.
Experiments show that strong performance on a single, static graph does not translate to good inductive generalization to unseen graphs, domains, and tasks.
Graph meta-learners trained with the proposed few-shot subgraph sampling outperform standard community models in the inductive setup, demonstrating the importance of rapid adaptation capabilities.

Estatísticas

The paper reports the following key statistics:

GossipCop dataset: 17,617 documents, 29,229 users, 2,334,554 edges
CoAID dataset: 947 documents, 4,059 users, 61,254 edges
TwitterHateSpeech dataset: 16,201 documents, 1,875 users, 65,600 edges

Citações

"Community models for malicious content detection are models that operate on social graphs, i.e., graphs of content and users. They 1) classify content nodes as malicious or not, 2) incorporate information from interacting users in the graph when doing so, and 3) leverage emergent network properties like homophily to boost detection performance."
"Evidently, there exists a mismatch in the performance of community models on research datasets and in more realistic application settings. Research datasets are static; they capture a view of the social graph weeks or months after relevant content has been introduced and spread."

Principais Insights Extraídos De

A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection

by Ivo Verhoeve... às arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01822.pdf

Perguntas Mais Profundas

How can the proposed few-shot subgraph sampling approach be extended to incorporate temporal dynamics of online content and communities?

The few-shot subgraph sampling approach can be extended to incorporate temporal dynamics by introducing a time component to the sampling process. This can be achieved by considering the timestamps associated with the interactions between users and content in the social graph. Here are some ways to incorporate temporal dynamics:

Temporal Sampling: When selecting anchor users for subgraph sampling, prioritize users who have interacted with recent content. This ensures that the sampled subgraphs capture the most recent dynamics of the online community.

Dynamic Window: Define a dynamic time window within which the subgraph sampling occurs. This window can slide over time, allowing the model to adapt to changing trends and patterns in the social graph.

Time-aware Features: Include time-related features in the node representations to capture the temporal context of interactions. These features can help the model understand how the relationships between users and content evolve over time.

Temporal Attention Mechanisms: Implement attention mechanisms that give more weight to recent interactions in the subgraph sampling process. This way, the model can focus on the most relevant temporal information.

By incorporating these temporal dynamics into the few-shot subgraph sampling approach, the model can better adapt to the evolving nature of online content and communities over time.

How can the potential biases and limitations introduced by the user-centric sampling strategy be mitigated?

The user-centric sampling strategy may introduce biases and limitations that can impact the generalization and performance of the model. Here are some ways to mitigate these biases:

Diverse Anchor Selection: Ensure diversity in the selection of anchor users for subgraph sampling. Avoid bias towards specific user profiles or content types by randomly selecting anchor users from different segments of the social graph.

Balanced Label Distribution: Maintain a balanced distribution of labels within the sampled subgraphs. This prevents the model from being skewed towards specific classes or types of content.

User Representation Learning: Instead of zero-initializing user nodes, consider incorporating user features or embeddings based on their interactions and behavior in the social graph. This can provide more context and reduce anonymity biases.

Bias Correction Techniques: Implement bias correction techniques during training to counteract any inherent biases introduced by the sampling strategy. This can include re-weighting samples or adjusting gradients based on bias analysis.

Cross-validation and Evaluation: Conduct thorough cross-validation and evaluation on diverse datasets to assess the model's performance across different user profiles and content types. This helps in identifying and addressing biases that may arise from the sampling strategy.

By implementing these mitigation strategies, the user-centric sampling approach can be optimized to reduce biases and limitations, leading to more robust and generalizable models for malicious content detection.

How can the insights from this work on community models be applied to develop more generalizable content-only malicious content detection models?

The insights from this work on community models can be leveraged to enhance the generalizability of content-only malicious content detection models. Here are some ways to apply these insights:

Incorporating Graph Context: Integrate graph context into content-only models by considering the relationships between users and content. This can be achieved by using graph neural networks to capture the social graph structure and interactions.

Few-shot Learning: Implement few-shot learning techniques in content-only models to adapt to new tasks, domains, and content forms with limited labeled examples. This allows the model to generalize better to unseen data.

Meta-learning Approaches: Explore meta-learning approaches in content-only models to enable rapid adaptation and generalization to evolving content and communities. Meta-learners can learn to learn from few-shot episodes and improve performance on new tasks.

Temporal Dynamics: Incorporate temporal dynamics of online content into content-only models to capture the evolving nature of malicious content. Consider time-aware features and attention mechanisms to model changes over time.

Bias Mitigation: Apply bias mitigation strategies learned from community models to address biases in content-only models. Ensure balanced label distribution, diverse sampling, and bias correction techniques to improve model fairness and performance.

By integrating these insights into content-only malicious content detection models, researchers and practitioners can develop more robust and generalizable models that are better equipped to tackle the challenges of detecting and mitigating malicious content online.