Einblick - Computer Networks - # Discourse Analysis of the 2023 Israel-Hamas Conflict on Reddit

Comprehensive Dataset on Reddit Discussions of the 2023 Israel-Hamas Conflict

Q: How might the findings from this dataset be used to promote more constructive and inclusive dialogue around the Israel-Hamas conflict?

The insights derived from the IsamasRed dataset can play a crucial role in fostering more constructive and inclusive dialogue surrounding the Israel-Hamas conflict. By analyzing the conversations and comments on Reddit, researchers can identify key themes, sentiments, and controversial topics that dominate the discourse. This understanding can be leveraged to develop strategies for promoting respectful and informed discussions on the conflict. Identifying Common Ground: By analyzing the topics and sentiments expressed in the dataset, researchers can pinpoint areas of common ground between different perspectives. Highlighting shared values or concerns can serve as a starting point for building bridges and promoting understanding among conflicting parties. Addressing Misinformation: The dataset can help in identifying instances of misinformation or biased narratives that contribute to polarization. By debunking false information and promoting fact-based discussions, stakeholders can work towards a more informed dialogue. Encouraging Empathy: Understanding the emotional and moral dimensions of the discourse can help in fostering empathy among participants. By acknowledging and validating diverse emotions and perspectives, stakeholders can create a more empathetic and inclusive dialogue space. Facilitating Diverse Voices: The dataset can shed light on the representation of different voices and viewpoints in the discussions. Efforts can be made to amplify marginalized voices, promote diversity of opinions, and ensure that all perspectives are heard and respected. Promoting Constructive Engagement: Insights from the dataset can guide the development of guidelines for constructive engagement, such as respectful communication, active listening, and open-mindedness. Encouraging participants to engage in dialogue rather than debate can lead to more productive conversations.

Q: What are the potential biases or limitations in the automated keyword extraction and data collection process, and how could they be addressed in future research?

Automated keyword extraction and data collection processes, while efficient, can introduce biases and limitations that need to be carefully addressed in future research. Some potential biases and limitations include: Language Model Biases: The use of large language models like GPT-4 for keyword extraction may inherit biases present in the training data. Future research should focus on mitigating these biases through diverse training data and bias detection algorithms. Keyword Relevance: Automated keyword extraction may not always capture the nuanced context of the conflict, leading to the inclusion or exclusion of relevant terms. Researchers can address this limitation by refining the keyword extraction algorithms and incorporating human validation processes. Multilingual Discourse: The English-centric approach of keyword extraction may overlook valuable insights from non-English discussions on the conflict. Future research should consider multilingual keyword extraction to capture a more comprehensive view of the discourse. Discrepancy in Term Usage: Differences in term usage between Wikipedia and Reddit can impact the effectiveness of keyword extraction. Researchers can address this limitation by refining the keyword selection process and incorporating feedback mechanisms for keyword validation. Data Deficiency: Reliance on third-party sources for data collection can result in data deficiencies, as observed in the glitch from August 20th to August 30th. Future research should explore multiple data sources and implement robust data validation procedures to ensure data completeness. Addressing these biases and limitations requires a combination of algorithmic improvements, validation processes, and a commitment to transparency and ethical data collection practices.

Kernkonzepte

This study presents a comprehensive dataset, IsamasRed, comprising over 400,000 conversations and 8 million comments from Reddit related to the 2023 Israel-Hamas conflict. The dataset is curated using an innovative keyword extraction framework that leverages a large language model to identify relevant terms, enabling a thorough analysis of the online discourse surrounding this geopolitical conflict.

Zusammenfassung

The researchers introduce IsamasRed, a dataset that tracks Reddit discussions on the 2023 Israel-Hamas conflict. The dataset was compiled using an automated keyword extraction framework that leverages a large language model to identify relevant terms, ensuring a comprehensive data collection.

The dataset contains over 400,000 conversations and 8 million comments spanning from August to November 2023, a period that saw a significant escalation of the conflict between Israel and Hamas. The researchers also curated two subsets of the dataset, IsamasRed-Z and IsamasRed-P, focusing on discussions related to Zionism/antisemitism and the "Free Palestine" movement/Islamophobia, respectively.

The researchers conducted a thorough analysis of the dataset, examining various aspects of the online discourse. They looked at user engagement metrics, such as popularity scores and the number of unique authors, to understand the level of interest and participation in the discussions. The analysis also explored the controversial nature of the discourse, using Reddit's built-in controversiality indicator to measure the intensity of debates.

To gain insights into the ethical and emotional dimensions of the discussions, the researchers employed state-of-the-art transformer models to detect moral sentiments and emotions expressed in the comments. The findings reveal that the discourse was dominated by negative emotions, such as anger, fear, and disgust/contempt, reflecting the charged and polarized nature of the discussions.

Overall, the IsamasRed dataset and the accompanying analysis provide a rich, contextually informed understanding of the digital discourse surrounding the 2023 Israel-Hamas conflict, offering valuable insights into the complex interplay between ideology, sentiment, and community engagement in online spaces.

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Statistiken

The conflict escalated on October 7, 2023, with Hamas militants launching a wide-ranging assault, resulting in unprecedented Israeli casualties.
The dataset contains over 400,000 conversations and 8 million comments from Reddit, spanning from August to November 2023.
The two subsets, IsamasRed-Z and IsamasRed-P, focus on discussions related to Zionism/antisemitism and the "Free Palestine" movement/Islamophobia, respectively.

Zitate

"The conflict between Israel and Palestinians significantly escalated after the October 7, 2023 Hamas attack, capturing global attention."
"The mass demonstrations and counter demonstrations across the world exposed long-simmering grievances and cultural fault lines."
"Loud voices exacerbated deep divisions and generational divides about the legitimacy of Israel vs the legitimacy of Palestine, right to self-defense and self-determination, Zionism vs Palestinian rights, antisemitism vs Islamophobia, etc."

Wichtige Erkenntnisse aus

IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict

by Kai Chen,Zih... um arxiv.org 04-18-2024

https://arxiv.org/pdf/2401.08202.pdf

IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict

Tiefere Fragen

How might the findings from this dataset be used to promote more constructive and inclusive dialogue around the Israel-Hamas conflict?

The insights derived from the IsamasRed dataset can play a crucial role in fostering more constructive and inclusive dialogue surrounding the Israel-Hamas conflict. By analyzing the conversations and comments on Reddit, researchers can identify key themes, sentiments, and controversial topics that dominate the discourse. This understanding can be leveraged to develop strategies for promoting respectful and informed discussions on the conflict.

Identifying Common Ground: By analyzing the topics and sentiments expressed in the dataset, researchers can pinpoint areas of common ground between different perspectives. Highlighting shared values or concerns can serve as a starting point for building bridges and promoting understanding among conflicting parties.

Addressing Misinformation: The dataset can help in identifying instances of misinformation or biased narratives that contribute to polarization. By debunking false information and promoting fact-based discussions, stakeholders can work towards a more informed dialogue.

Encouraging Empathy: Understanding the emotional and moral dimensions of the discourse can help in fostering empathy among participants. By acknowledging and validating diverse emotions and perspectives, stakeholders can create a more empathetic and inclusive dialogue space.

Facilitating Diverse Voices: The dataset can shed light on the representation of different voices and viewpoints in the discussions. Efforts can be made to amplify marginalized voices, promote diversity of opinions, and ensure that all perspectives are heard and respected.

Promoting Constructive Engagement: Insights from the dataset can guide the development of guidelines for constructive engagement, such as respectful communication, active listening, and open-mindedness. Encouraging participants to engage in dialogue rather than debate can lead to more productive conversations.

What are the potential biases or limitations in the automated keyword extraction and data collection process, and how could they be addressed in future research?

Automated keyword extraction and data collection processes, while efficient, can introduce biases and limitations that need to be carefully addressed in future research. Some potential biases and limitations include:

Language Model Biases: The use of large language models like GPT-4 for keyword extraction may inherit biases present in the training data. Future research should focus on mitigating these biases through diverse training data and bias detection algorithms.

Keyword Relevance: Automated keyword extraction may not always capture the nuanced context of the conflict, leading to the inclusion or exclusion of relevant terms. Researchers can address this limitation by refining the keyword extraction algorithms and incorporating human validation processes.

Multilingual Discourse: The English-centric approach of keyword extraction may overlook valuable insights from non-English discussions on the conflict. Future research should consider multilingual keyword extraction to capture a more comprehensive view of the discourse.

Discrepancy in Term Usage: Differences in term usage between Wikipedia and Reddit can impact the effectiveness of keyword extraction. Researchers can address this limitation by refining the keyword selection process and incorporating feedback mechanisms for keyword validation.

Data Deficiency: Reliance on third-party sources for data collection can result in data deficiencies, as observed in the glitch from August 20th to August 30th. Future research should explore multiple data sources and implement robust data validation procedures to ensure data completeness.

Addressing these biases and limitations requires a combination of algorithmic improvements, validation processes, and a commitment to transparency and ethical data collection practices.

Given the complex historical and political context of the Israel-Hamas conflict, how might the insights from this dataset inform broader discussions on the role of social media in shaping public discourse around geopolitical issues?

The insights from the IsamasRed dataset can offer valuable lessons and perspectives on the role of social media in shaping public discourse around geopolitical issues, particularly in the context of the Israel-Hamas conflict. These insights can inform broader discussions in the following ways:

Understanding Information Dissemination: The dataset can shed light on how information, narratives, and opinions are disseminated and amplified through social media platforms. By analyzing the spread of content and engagement patterns, researchers can understand the mechanisms through which social media influences public discourse.

Analyzing Polarization and Controversy: The dataset can provide insights into the polarization and controversy that often characterize discussions on geopolitical issues. By examining the nature of debates, the prevalence of conflicting viewpoints, and the factors that contribute to polarization, researchers can better understand the dynamics of online discourse.

Exploring Emotional and Moral Dimensions: The dataset's analysis of emotional and moral sentiments in discussions can highlight the impact of these dimensions on public discourse. By studying how emotions and moral foundations shape opinions and interactions, researchers can uncover the underlying drivers of online conversations.

Promoting Media Literacy: Insights from the dataset can inform efforts to promote media literacy and critical thinking skills among social media users. By highlighting the prevalence of misinformation, bias, and emotional manipulation in online discourse, stakeholders can advocate for media literacy education and fact-checking initiatives.

Guiding Policy and Regulation: The dataset can inform policymakers, platform developers, and regulators about the challenges and opportunities presented by social media in shaping public discourse. By understanding the implications of social media on geopolitical discussions, stakeholders can develop policies and regulations to promote transparency, accountability, and responsible online behavior.

Overall, the insights from the IsamasRed dataset can serve as a valuable resource for examining the complex interplay between social media, public discourse, and geopolitical issues, offering important lessons for shaping informed and constructive conversations in the digital age.