An Interactive Framework for Rapidly Profiling News Media Sources on Emerging Events

핵심 개념

An interactive framework that combines the strengths of graph-based news media profiling models, pre-trained large language models, and human insight to rapidly characterize the social context on social media and detect fake and biased news media sources, even in the challenging settings of emerging news events.

초록

The paper proposes an interactive framework for news media profiling that leverages the strengths of graph-based models, pre-trained large language models (LLMs), and human insight. The key aspects of the framework are:

Initial Candidate Communities: The framework first forms initial candidate information communities by clustering social media users based on their graph model embeddings, capturing model beliefs about user relationships and perspectives.

User Summarization and Human Validation: As the candidate communities are likely imperfect, the framework uses LLMs to summarize each user in the community, facilitating human validation. Humans read the user summaries and determine which users are similar, forming validated communities.

Automated Community Expansion: The validated communities are then expanded by the framework, adding new users to them based on model clustering. These new user assignments are validated using LLMs, which are prompted with the human-validated communities as training examples for a simpler user similarity detection task.

Iterative Community Formation: For users not assigned to validated communities, the above process is repeated, expanding the number of communities. This iterative process continues until all users are assigned.

Unsupervised Graph Training: Finally, the framework fine-tunes the graph model in an unsupervised manner, using the validated user communities to learn improved node embeddings, which are then used for news media profiling.

The framework is evaluated on the challenging task of fake news and news source bias detection in the emerging news events setting, where test data is completely unseen. Experiments show that with less than 5 human interactions, the framework can significantly improve performance over baselines, demonstrating the benefits of combining graph, LLM, and human strengths.

통계

The recent rise of social media has enabled the rapid spread of fake and biased news, affecting people's perspectives.
Detecting and profiling the sources that spread this news is important, but challenging for automated systems, especially in emerging news event settings.
The framework is evaluated on the Black Lives Matter and Abortion/Feminism news events, with test data completely unseen during training.

인용구

"The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs."
"Due to the struggles of AI systems to automatically profile news media, in this paper we propose a different, interactive approach, for this task."
"We hypothesize that users on social media form information communities, or groups of users, where certain themes circulate more in some communities vs. others."

핵심 통찰 요약

An Interactive Framework for Profiling News Media Sources

by Nikhil Mehta... 게시일 arxiv.org 04-30-2024

https://arxiv.org/pdf/2309.07384.pdf

더 깊은 질문

How can the interactive framework be extended to handle multilingual news media sources and diverse social media platforms beyond Twitter?

The interactive framework can be extended to handle multilingual news media sources and diverse social media platforms beyond Twitter by incorporating language translation capabilities and adapting the model to different social media platforms.

Language Translation: To handle multilingual news media sources, the framework can integrate language translation models to process and analyze content in different languages. This would involve translating user profiles, articles, and interactions into a common language for analysis. Models like multilingual BERT or language-specific translation models can be utilized for this purpose.

Adapting to Different Platforms: Each social media platform has its own unique characteristics and user behaviors. The framework can be adapted to different platforms by customizing the data collection process, feature engineering, and interaction mechanisms. For example, the model can be trained on data from platforms like Facebook, Instagram, or Reddit to capture diverse user behaviors and content types.

Cross-platform Analysis: The framework can also be designed to aggregate data from multiple social media platforms to provide a comprehensive analysis of news media sources across different platforms. This would involve developing connectors and data pipelines to collect and integrate data from various sources.

Multimodal Analysis: In addition to text-based analysis, the framework can incorporate multimodal analysis techniques to analyze images, videos, and other media formats present on social media platforms. This would provide a more holistic understanding of user interactions and content dissemination.

By incorporating these strategies, the interactive framework can be extended to handle multilingual news media sources and diverse social media platforms beyond Twitter, enabling a more comprehensive analysis of information dynamics in a global context.

How can the potential biases and limitations of the human interactors in the framework be mitigated?

Human interactors in the framework may introduce biases and limitations that can impact the accuracy and reliability of the analysis. To mitigate these issues, the following strategies can be implemented:

Diverse Interactor Selection: To reduce individual biases, a diverse set of human interactors with varying backgrounds, perspectives, and expertise can be involved in the validation process. This helps in obtaining a more comprehensive and balanced assessment of user communities.

Training and Guidelines: Providing thorough training to human interactors on the task, evaluation criteria, and potential biases can help standardize their judgments. Clear guidelines and decision-making frameworks should be established to ensure consistency in the validation process.

Interactor Consensus: Implementing a consensus mechanism where multiple interactors evaluate the same communities independently can help identify and resolve discrepancies. Consensus-based decision-making can enhance the reliability of the validation outcomes.

Regular Calibration and Feedback: Regular calibration sessions and feedback loops can be established to monitor interactor performance, address biases, and provide continuous improvement opportunities. Constructive feedback and performance evaluations can help enhance the quality of human judgments.

Bias Awareness and Mitigation: Interactors should be made aware of potential biases such as confirmation bias, cultural bias, or political bias that may influence their decisions. Encouraging critical thinking and self-reflection can help mitigate these biases during the validation process.

Random Sampling and Blind Testing: Random sampling of interactions and blind testing scenarios where interactors are unaware of the context can help assess their objectivity and consistency. This approach can provide insights into interactor reliability and identify areas for improvement.

By implementing these strategies, the potential biases and limitations of human interactors in the framework can be effectively mitigated, ensuring more accurate and unbiased analysis of user information communities.

How can the framework's insights about user information communities be leveraged to study the broader dynamics of information spread and opinion formation on social media?

The framework's insights about user information communities can be leveraged to study the broader dynamics of information spread and opinion formation on social media in the following ways:

Influence Analysis: By analyzing the connections and interactions within user communities, the framework can identify influential users or sources that drive information spread and opinion formation. Understanding the influence dynamics can help predict trends and viral content on social media platforms.

Topic Modeling and Trend Analysis: The framework's insights can be used to identify prevalent topics, discussions, and trends within different user communities. By tracking the evolution of topics and sentiments, researchers can gain insights into the formation of opinions and attitudes on social media.

Network Analysis: Leveraging the network structure of user communities, the framework can conduct network analysis to study information diffusion patterns, community polarization, and echo chamber effects. This can provide valuable insights into how information flows and opinions are reinforced within social media networks.

Sentiment Analysis: By analyzing user interactions and content shared within communities, sentiment analysis techniques can be applied to understand the emotional tone and attitudes prevalent in different user groups. This can help in identifying polarizing content and sentiment shifts over time.

Behavioral Studies: The framework's insights can be used to conduct behavioral studies on social media users, including information consumption patterns, engagement behaviors, and response mechanisms. This can shed light on how users form opinions, engage with content, and interact with diverse viewpoints.

Predictive Modeling: By leveraging the framework's insights as features, predictive models can be developed to forecast information spread, opinion changes, and community dynamics on social media. Machine learning algorithms can be trained to anticipate trends and behaviors based on historical data.

Overall, the framework's insights about user information communities provide a rich source of data for studying the complex dynamics of information spread and opinion formation on social media. By applying advanced analytical techniques and interdisciplinary approaches, researchers can gain a deeper understanding of how information shapes public discourse and influences societal perceptions.