RECORD
Explaining Controversy on Social Media via Stance Summarization
Information Retrieval · Submitted 20 Jun 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. In this light, there has been significant work that studies how to identify and measure controversy online. However, we currently lack a tool for effectively understanding controversy in social media. For example, users have to manually examine postings to find the arguments of conflicting stances that make up the controversy. In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances. We focus on Twitter and treat stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic. We formalize the characteristics of a good stance summary and propose a ranking model accordingly. We first evaluate our methods on five controversial topics on Twitter. Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. | background 1.00 | 0.05 | 0.02 | asserted 0.96 |
| s1 | In this light, there has been significant work that studies how to identify and measure controversy online. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s2 | However, we currently lack a tool for effectively understanding controversy in social media. | gap 1.00 | 0.08 | 0.03 | asserted 1.00 |
| s3 | For example, users have to manually examine postings to find the arguments of conflicting stances that make up the controversy. | gap 0.55 | 0.06 | 0.02 | asserted 1.00 |
| s4 | In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances. | contribution 1.00 | 0.75 | 0.09 | asserted 0.93 |
| s5 | We focus on Twitter and treat stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic. | method 0.94 | 0.85 | 0.04 | asserted 0.98 |
| s6 | We formalize the characteristics of a good stance summary and propose a ranking model accordingly. | method 0.86 | 0.95 | 0.05 | asserted 0.99 |
| s7 | We first evaluate our methods on five controversial topics on Twitter. | method 0.94 | 0.95 | 0.03 | asserted 0.92 |
| s8 | Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy. | result 1.00 | 0.96 | 0.81 | asserted 1.00 |
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The paper
Original abstract
In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. In this light, there has been significant work that studies how to identify and measure controversy online. However, we currently lack a tool for effectively understanding controversy in social media. For example, users have to manually examine postings to find the arguments of conflicting stances that make up the controversy. In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances. We focus on Twitter and treat stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic. We formalize the characteristics of a good stance summary and propose a ranking model accordingly. We first evaluate our methods on five controversial topics on Twitter. Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy.
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