Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis

Simaki, Vasiliki; Paradis, Carita; Kerren, Andreas

doi:10.1515/icame-2018-0007

Cited by 12 publications

(11 citation statements)

References 61 publications

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“…As a second step, we calculated the pair-wise combinations of the stance categories. Confirming our quantitative study [33], the contrariety and necessity binary classification achieved the best results with up to 71% accuracy. This result was encouraging and highlighted the fact that each stance category has a different level of distinctness, with contrariety and necessity as the most discriminative ones.…”

Section: Background Worksupporting

confidence: 80%

“…We compiled a manually annotated corpus according to this framework, and performed various qualitative, quantitative and computational tasks in order to identify patterns in language that are associated to each stance. We highlighted six out of ten stance categories as the most frequent ones in our corpus: contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty, and showed that stances such as contrariety and necessity, are more distinctive than other [33]. In this paper, we applied the corpus-, quantitative-, and computational-based features that were highlighted as important in our previous studies in a data set extracted from social media.…”

Section: Introductionmentioning

confidence: 89%

“…In the quantitative analysis of the Brexit Blog Corpus that followed [33], we aimed to identify features that determine the formal profiles of six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty) in a subset of the BBC. The study had two parts: firstly, it examined a large number of formal linguistic features such as punctuation, words and grammatical categories occurred in the sentences in order to describe the specific characteristics of each category, and secondly, it compared characteristics in the entire data set in order to determine stance similarities in the data set.…”

Section: Background Workmentioning

confidence: 99%

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Detection of Stance-Related Characteristics in Social Media Text

Simaki

Simakis²,

Paradis

et al. 2018

Proceedings of the 10th Hellenic Conference on Artificial Intelligence

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In this paper, we present a study for the identification of stancerelated features in text data from social media. Based on our previous work on stance and our findings on stance patterns, we detected stance-related characteristics in a data set from Twitter and Facebook. We extracted various corpus-, quantitative-and computational-based features that proved to be significant for six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty), and we tested them in our data set. The results of a preliminary clustering method are presented and discussed as a starting point for future contributions in the field. The results of our experiments showed a strong correlation between different characteristics and stance constructions, which can lead us to a methodology for automatic stance annotation of these data.

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Section: Background Worksupporting

confidence: 80%

Section: Introductionmentioning

confidence: 89%

Section: Background Workmentioning

confidence: 99%

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Detection of Stance-Related Characteristics in Social Media Text

Simaki

Simakis²,

Paradis

et al. 2018

Proceedings of the 10th Hellenic Conference on Artificial Intelligence

Self Cite

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“…Its discourse characteristics are widely discussed (e.g. [Simaki et al 2018]; [Berger, Hennig 2015]; [Germasheva 2010] etc.). It is generally assumed that it combines properties of written and spoken modes and, besides, manifests its own features.…”

Section: Introductionmentioning

confidence: 99%

Discourse Features of Blogs in Subcorpus of Russian Ru-Rstreebank

Toldova¹,

Davydova²,

Kobozeva³

et al. 2020

Computational Linguistics and Intellectual Technologies

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The paper presents a corpus study of the discourse features in the corpus of blogs. It is based on the data of Ru-RSTreebank annotated within the framework of the Rhetorical Structure theory [Mann, Thompson 1988]. The Ru-RSTreebank represents genres of news and popular science, scientific papers, and blogs texts. Blog subcorpus contains such topics as travelling, cosmetics, sports and health, psychology, IT and tech and some others. Blogs texts constitute a specific genre as they combine properties of written and spoken discourse. The purpose of the paper is to investigate discourse features of blogs in comparison with other genres. We analyze the variation in rhetoric relations distribution among genres, and single out the differences in discourse connectives usage. Furthermore, we check the distribution of other discourse features reported in different studies for spoken discourse and for social media in the Ru-RSTreebank blogs subcorpus. The general frequency analysis and the experiments on RandomForest classifier application to genre recognition have shown that the most important rhetoric relations specific to blogs are Evaluation and Contrast, that there is a tendency to use shorter discourse units and not to express the discourse relations overtly via subordinative conjunctions.

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“…In this paper, we investigate multilabel stance detection in the context of three datasets: the Brexit Blog Corpus (BBC) (Simaki et al, 2018), the US Election Tweets Corpus (ETC) (Sobhani et al, 2019), and the Moral Foundations Twitter Corpus (MFTC) (Dehghani et al, 2019). Figure 1 shows examples from each dataset where the utterances have been annotated with multiple stances.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Label Dependencies in Multilabel Stance Detection

Ferreira¹,

Vlachos

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Stance detection in social media is a wellstudied task in a variety of domains. Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are mutually exclusive, and typically positive, negative or neutral. In this paper, we address versions of the task in which an utterance can have multiple labels, thus corresponding to multilabel classification. We propose a method that explicitly incorporates label dependencies in the training objective and compare it against a variety of baselines, as well as a reduction of multilabel to multiclass learning. In experiments with three datasets, we find that our proposed method improves upon all baselines on two out of three datasets. We also show that the reduction of multilabel to multiclass classification can be very competitive, especially in cases where the output consists of a small number of labels and one can enumerate over all label combinations.

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Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis

Cited by 12 publications

References 61 publications

Detection of Stance-Related Characteristics in Social Media Text

Detection of Stance-Related Characteristics in Social Media Text

Discourse Features of Blogs in Subcorpus of Russian Ru-Rstreebank

Incorporating Label Dependencies in Multilabel Stance Detection

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