Corpus ID: 102487723

Unsupervised User Stance Detection on Twitter

@article{Darwish2020UnsupervisedUS,
  title={Unsupervised User Stance Detection on Twitter},
  author={Kareem Darwish and P. Stefanov and M. Aupetit and Preslav Nakov},
  journal={ArXiv},
  year={2020},
  volume={abs/1904.02000}
}
We present a highly effective unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics. In particular, we use dimensionality reduction to project users onto a low-dimensional space, followed by clustering, which allows us to find core users that are representative of the different stances. Our framework has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification. First, we do not… Expand
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45 Citations
Highly Influential Citations
2
Background Citations
27
Methods Citations
14

45 Citations

Citation Type

Citation Type

A Few Topical Tweets are Enough for Effective Stance Detection
  • 2020
Stance detection entails ascertaining the position of a user towards a target, such as an entity, topic, or claim. Recent work that employs unsupervised classification has shown that performing… Expand
A Few Topical Tweets are Enough for Effective User-Level Stance Detection
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This paper improves user-level stance detection on vocal Twitter users by representing tweets using contextualized embeddings, which capture latent meanings of words in context and performs unsupervised classification of the user, which entails clustering a user with other users in the training set. Expand
A Few Topical Tweets are Enough for Effective User Stance Detection
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This paper improves user-level stance detection on vocal Twitter users by representing tweets using contextualized embeddings, which capture latent meanings of words in context and performs unsupervised classification of the user, which entails clustering a user with other users in the training set. Expand
Predicting the Topical Stance of Media and Popular Twitter Users
TLDR
This paper proposes a method that uses unsupervised learning and is able to characterize both the general political leaning of online media and popular Twitter users, as well as their stances with respect to controversial topics, by leveraging on the retweet behavior of users. Expand
Predicting the Topical Stance and Political Leaning of Media using Tweets
TLDR
A cascaded method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior; then, it uses supervised learning based on user labels to characterize both the general political leaning of online media and of popular Twitter users. Expand
Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media
TLDR
These findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic. Expand
Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey
TLDR
This paper presents an unsupervised method for target-specific stance detection in a polarized setting, specifically Turkish politics, achieving 90% precision in identifying user stances, while maintaining more than 80% recall. Expand
Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs
TLDR
This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets, and seeks to understand retweet cascades. Expand
Unsupervised Discovery Of Semantically Aware Communities With Tensor Kruskal Decomposition: A Case Study In Twitter
TLDR
A tensor representation for Twitter subgraphs is proposed which takes into consideration both the followfollower relationships but also the coherency in hashtags, and which clearly outperforms the SVD in terms of finding a more compact community size distribution in experiments done in Julia on a Twitter sub graph. Expand
tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets
TLDR
This work finds that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence, and investigates the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Expand
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