NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets

Misra, Amita; Ecker, Brian; Handleman, Theodore; Hahn, Nicolas; Walker, Marilyn A.

doi:10.18653/v1/s16-1068

Cited by 19 publications

(13 citation statements)

References 17 publications

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“…Elsewhere, we present algorithms for automatically identifying and bootstrapping arguments with different properties. We have methods to extract arguments that represent different stances on an issue (Misra et al, 2016a;Anand et al, 2011;Sridhar et al, 2015;Walker et al, 2012a;Walker et al, 2012b), as well as argument exchanges that are agreements vs. disagreements , factual vs. emotional arguments (Oraby et al, 2015), sarcastic and not-sarcastic arguments, and nasty vs. nice arguments (Oraby et al, 2016;Lukin and Walker, 2013;Justo et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Lukin¹,

Anand²,

Walker³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.

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Section: Discussionmentioning

confidence: 99%

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Lukin¹,

Anand²,

Walker³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

Self Cite

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“…In order to reduce this dependency on labeled dataset, there is a line of research works that have proposed machine learning frameworks that require weak supervision. Weak supervised stance detection works rely on different characteristics related to an event, like stance related information from annotated hashtags [13,24,32], user behavior and information [16,22,42], specific key-phrases related to events [19] and topical information [12]. However, all of these approaches require annotated event-specific twitter handles, hashtags or key-phrases.…”

Section: Weak Supervised Approachmentioning

confidence: 99%

Detecting Stance in Tweets : A Signed Network based Approach

Chakraborty¹,

Bhavsar²,

Dandapat³

et al. 2022

Preprint

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Identifying user stance related to a political event has several applications, like determination of individual stance, shaping of public opinion, identifying popularity of government measures and many others. The huge volume of political discussions on social media platforms, like, Twitter, provide opportunities in developing automated mechanisms to identify individual stance and subsequently, scale to a large volume of users. However, issues like short text and huge variance in the vocabulary of the tweets make such exercise enormously difficult. Existing stance detection algorithms require either event specific training data or annotated twitter handles and therefore, are difficult to adapt to new events. In this paper, we propose a sign network based framework that use external information sources, like news articles to create a signed network of relevant entities with respect to a news event and subsequently use the same to detect stance of any tweet towards the event. Validation on 5, 000 tweets related to 10 events indicates that the proposed approach can ensure over 6.5% increase in average F1 score compared to the existing stance detection approaches.

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“…Another approach to the SemEval 2016 task, Misra et al (2016) retrieved tweets with hashtags that were "stancebearing on their own", and labelled them accordingly. These tweets were then used as labelled examples to train a classifier.…”

Section: Relevant Workmentioning

confidence: 99%

You can simply rely on communities for a robust characterization of stances

Furman¹,

Marro²,

Cardellino

et al. 2021

FLAIRS

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We show that the structure of communities in social me- dia provides robust information for weakly supervised approaches to assign stances to tweets. Using as seed the SemEval 2016 Stance Detection Task annotated data, we retrieved a high number of topically related tweets. We then propagated information from the manually an- notated seed to the retrieved tweets and thus obtained a bigger training corpus. Classifiers trained with this bigger, weakly supervised dataset reach similar or better performance than those trained with the manually annotated seed. In addition, they are more robust with respect to common manual annotator errors or biases and they have arguably more coverage than smaller datasets. Weakly supervised approaches, most notably self- supervision, commonly suffer from error propagation. Interestingly, communities seem to provide a structure that constrains error propagation. In particular, weakly supervised classifiers that incorporate community struc- ture are more robust with respect to class imbalance. Additionally, this is a straightforward, transparent ap- proach, using standard tools and pipelines, cheaper and faster than methods like crowd sourcing for manual an- notations. Thus it facilitates adoption, interpretability and accountability.

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NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets

Cited by 19 publications

References 17 publications

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Detecting Stance in Tweets : A Signed Network based Approach

You can simply rely on communities for a robust characterization of stances

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