Stance-In-Depth Deep Neural Approach to Stance Classification

Rajendran, Gayathri; Chitturi, Bhadrachalam; Poornachandran, Prabaharan

doi:10.1016/j.procs.2018.05.132

Cited by 25 publications

(16 citation statements)

References 8 publications

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“…from the textual data. It is identified through understanding the similarity of the headline and body of news content or article [22]. Common approaches involve training a labeled dataset with their stances, but a challenging task in this area includes stance detection without having the target values or no training data.…”

Section: Stance Detectionmentioning

confidence: 99%

A Novel Stance based Sampling for Imbalanced Data

Agarwal¹,

Rana²,

Jariwala³

et al. 2022

IJACSA

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While the world is suffering from coronavirus pandemic (COVID-19), a parallel battle with Infodemic, the proliferation of fake news online is also taking place. The spread of fake news during this global pandemic COVID-19 has dangerous consequences. This is the driving force behind this study. Relying on incorrect information obtained from the internet or social media can be fatal. According to a World Health Organization survey, at least 800 people have lost their lives because of COVID-19 misinformation during this time, highlighting the accurate automated classification of fake news. However, the data at disposal for classification is imbalanced. The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant. This imbalance leads to incorrect classification. The paper studies alternative approaches to text sampling. In this paper, we propose a stance based sampling method for balancing news data. The disparity between the title and content of news items is utilized to sample data points selectively and rectify the imbalance. The key findings are that the proposed stance-based sampling strategies enhance categorisation task performance consistently for varying degrees of imbalance. The proposed techniques can better detect misleading news in the health care sector.

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Section: Stance Detectionmentioning

confidence: 99%

A Novel Stance based Sampling for Imbalanced Data

Agarwal¹,

Rana²,

Jariwala³

et al. 2022

IJACSA

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show abstract

“…In the continuous attempt to limit the number of handcrafted features and corpus-specific knowledge, research has mainly shifted towards Deep Learning. The most common architecture is bidirectional Long-Short Term Memory (LSTM) Neural Networks fed with word embeddings [23,24,7]. In general, Deep Learning frameworks tend to give state-of-the-art results, which approach human performance.…”

Section: Related Work On Argument Identificationmentioning

confidence: 99%

Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach

Wambsganss¹,

Molyndris²,

Söllner³

2020

WI2020 Zentrale Tracks

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Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-laborintensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a twofold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge.

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“…Rajendran et al compared and discussed the performance of LSTM and GRU in stance detection whose categories are 'Agree', 'Discuss', 'Disagree', and 'Unrelated'. They found that bidirectional LSTM performed best [24]. Sobhani et al proposed an attentionbased encoder-decoder framework that shows better results than other methods in multi-target stance detection [25].…”

Section: Related Workmentioning

confidence: 99%

Stance Detection of Microblog Text Based on Two-Channel CNN-GRU Fusion Network

Wang

2019

IEEE Access

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In recent years, stance detection has become an important topic in the field of natural language processing. In earlier work, researchers have used feature engineering for stance detection but they need to define and extract appropriate features according to the particular application. This leads to poor generalization and a complex modeling process. Other researchers have applied deep learning methods. However, the popular convolutional neural network (CNN) method has the problem of information loss and a single-size CNN filter cannot accurately extract features that have different lengths from text, and so cannot deal with the diverse nature of features. In order to address these problems, we propose a two-channel CNN-GRU fusion network. First, a convolution layer with two filters with different window sizes is used to extract local features within the topic content and text content. Then, a gated recurrent unit (GRU) network is used to extract their timing characteristics. After that, the intermediate features are spliced and input to a classifier to complete the stance detection. Our method is validated using data from NLPCC 2016. The experimental results show that ACC and average F1 score of this method are 13.1% and 15.6% better than SVM method, 6.2% and 11.6% better than CNN method, 5.6% and 3.3% better than GRU method, and 1.1% and 2.2% better compared with hybrid model proposed by Nanyu, respectively, which is used as a baseline with no increase in run-time, and achieves the same accuracy with less run-time than another baseline of a semantic attention-based model proposed by Zhou. In addition, our method allows better classification than the single channel model. Finally, we find that the operation time of a multi-channel CNN-GRU increases gradually with increasing number of channels, but the classification accuracy does not improve, so a two-channel CNN-GRU is the most appropriate choice.

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Stance-In-Depth Deep Neural Approach to Stance Classification

Cited by 25 publications

References 8 publications

A Novel Stance based Sampling for Imbalanced Data

A Novel Stance based Sampling for Imbalanced Data

Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach

Stance Detection of Microblog Text Based on Two-Channel CNN-GRU Fusion Network

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