ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Contributors

Mennatallah El-Assady, Rita Sevastjanova, Daniel Keim, and Christopher Collins

Abstract

We present ThreadReconstructor, a visual analytics approach for detecting and analyzing the implicit conversational structure of discussions, e.g., in political debates and forums. Our work is motivated by the need to reveal and understand single threads in massive online conversations and verbatim text transcripts. We combine supervised and unsupervised machine learning models to generate a basic structure that is enriched by user-defined queries and rule-based heuristics. Depending on the data and tasks, users can modify and create various reconstruction models that are presented and compared in the visualization interface. Our tool enables the exploration of the generated threaded structures and the analysis of the untangled reply-chains, comparing different models and their agreement. To understand the inner-workings of the models, we visualize their decision spaces, including all considered candidate relations. In addition to a quantitative evaluation, we report qualitative feedback from an expert user study with four forum moderators and one machine learning expert, showing the effectiveness of our approach.

Publications

  • M. El-Assady, R. Sevastjanova, D. A. Keim, and C. Collins, “ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics,” Computer Graphics Forum, vol. 37, iss. 3, 2018.
    [Bibtex] [PDF]
    @Article{ela2018a,
      author = {Mennatallah El-Assady and Rita Sevastjanova and Daniel A. Keim and Christopher Collins},
      journal = {Computer Graphics Forum},
      publisher = {The Eurographics Association and John Wiley \& Sons Ltd.},
      title = {{ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics}},
      volume = 37,
      number = 3,
      year = {2018},
    }

Video

Research

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics

Detecting Negative Emotion for Mixed Initiative Visual Analytics

EduApps – Supporting Non-Native English Speakers to Overcome Language Transfer Effects

Metatation: Annotation as Implicit Interaction to Bridge Close and Distant Reading

DataTours: A Data Narratives Framework

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Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Abbreviating Text Labels on Demand

NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

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PivotSlice

Reading Comprehension on Mobile Devices

#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

Lexichrome

SentimentState: Exploring Sentiment Analysis on Twitter

Facilitating Discourse Analysis with Interactive Visualization

DimpVis

Glidgets

TandemTable

Simple Multi-Touch Toolkit

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Investigating the Semantic Patterns of Passwords

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Parallel Tag Clouds to Explore Faceted Text Corpora

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DocuBurst: Visualizing Document Content using Language Structure

Tabletop Text Entry Techniques

Lattice Uncertainty Visualization: Understanding Machine Translation and Speech Recognition

WordNet Visualization

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