Annotated Twitter Conversations: A Dataset for Argument Mining

Core Concepts

The core message of this paper is to present TACO, the first ground truth dataset for conversation-based argument mining on Twitter, along with a purpose-built annotation framework and a baseline classification model.

Abstract

The paper presents TACO, a dataset of 1,814 annotated tweets covering 200 entire Twitter conversations across six heterogeneous topics. The authors developed a specialized annotation framework based on the Cambridge Dictionary's definitions of inference and information to identify arguments in tweets.
The key highlights and insights are:

Annotation Framework: The authors define and identify arguments in tweets based on the presence of inference (Statement, Reason) or the lack thereof (Notification, None).

Conversation-Based Ground Truth Data: TACO is the first dataset of its kind, providing fully annotated Twitter conversations with a high agreement score of 0.718 Krippendorff's α among six experts.

Baseline Classification Model: The authors provide a transformer-based classifier that achieves an 85.06% macro F1 score for detecting arguments and 72.49% macro F1 for identifying combinations of inference and information in tweets.

Conversational Reply Patterns: Analysis of TACO reveals that users tend to reply with informed inferences (Reason) or additional information (Notification), reflecting a preference for informed debates.

The dataset and classifier serve as valuable resources for future research in argument mining on Twitter, enabling the training of models to manage tweets based on inference and information elements, as well as providing insights into the conversational dynamics of online discussions.

Stats

"Men shouldn't be making laws about women's bodies #abortion #Texas"
"'Bitter truth': EU chief pours cold water on idea of Brits keeping EU citizenship after #Brexit https://t.co/j3DteyWcMg via @TheLocalEurope"
"Opinion: As the draconian (and then some) abortion law takes effect in #Texas, this is not an idle question for millions of Americans. A slippery slope towards more like-minded Republican state legislatures to try to follow suit. #abortion #F24 https://t.co/sMKUdhRF1q"

Quotes

"Twitter has emerged as a global hub for engaging in online conversations and as a research corpus for various disciplines that have recognized the significance of its user-generated content."
"Argument mining has emerged as a valuable technique to identify the structure of inference and reasoning presented as arguments in natural language and is closely related to information extraction, fact checking, citation and opinion mining."

Key Insights Distilled From

TACO -- Twitter Arguments from COnversations

by Marc Feger,S... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00406.pdf

Deeper Inquiries

How can the proposed annotation framework be extended to capture more nuanced aspects of argumentation, such as the strength or persuasiveness of arguments?

The proposed annotation framework can be extended to capture more nuanced aspects of argumentation by incorporating additional layers of annotation that focus on the strength or persuasiveness of arguments. This can be achieved by introducing criteria or scales that assess the level of evidence, logical reasoning, emotional appeal, and rhetorical strategies used in each argument. Experts can be trained to evaluate these elements and assign scores or labels that indicate the strength of the argument. By integrating these nuanced aspects into the annotation framework, researchers can gain deeper insights into the quality and effectiveness of arguments present in online discourse.

What are the potential biases or limitations in the TACO dataset, and how can they be addressed to ensure more comprehensive and representative coverage of online discourse?

One potential bias in the TACO dataset could be the selection of topics, which may not fully represent the diversity of discussions on Twitter. To address this, researchers can expand the dataset to include a broader range of topics and hashtags, ensuring a more comprehensive coverage of online discourse. Additionally, there may be biases in the annotation process, such as subjective interpretations of what constitutes an argument. To mitigate this, inter-annotator agreement can be improved through rigorous training, clear guidelines, and regular calibration sessions among annotators. Moreover, efforts should be made to include diverse perspectives and voices in the annotation process to reduce bias and enhance the dataset's representativeness.

Given the dynamic and evolving nature of social media conversations, how can argument mining techniques be adapted to handle the continuous stream of new data and emerging topics on platforms like Twitter?

To adapt argument mining techniques to handle the continuous stream of new data and emerging topics on platforms like Twitter, researchers can implement real-time monitoring and analysis systems that automatically update and retrain models as new data becomes available. This can involve the use of streaming algorithms that process incoming tweets in real-time, extract relevant information, and update the classification models accordingly. Additionally, researchers can leverage techniques such as active learning to prioritize the annotation of new data points that are most informative for model improvement. By integrating these adaptive strategies, argument mining techniques can stay relevant and effective in capturing the ever-changing landscape of social media conversations.