DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs

Vijayaraghavan, Prashanth; Sysoev, I. A.; Vosoughi, Soroush; Roy, Deb

doi:10.48550/arxiv.1606.05694

Cited by 8 publications

(10 citation statements)

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“…Cosine distance between embeddings of reference source tweets and those of unlabeled candidate tweets is used as a measurement of semantic similarity. Cosine similarity between vector representation of two sentences is a common metric for measuring semantic similarity [20]. Two semantically equivalent embeddings have a cosine similarity of 1, and two vectors with no relation have that of 0.…”

Section: A Overview Of the Proposed Methodsmentioning

confidence: 99%

“…In particular, character-level CNNs trained on augmented data achieves the best performance. Recent research [19,20] applies this method to tweets, and shows that data augmentation can bring performance gains in deep learning tasks on noisy and short social media texts. Vosoughi et al [19] augment domainindependent English tweets for training an encoder-decoder embedding model built with character-level CNN and long short-term memory (LSTM).…”

Section: Introductionmentioning

confidence: 99%

“…The number of tweets before data augmentation is not presented, but the author report that 3 million tweets in total are available after data augmentation. Another work [20] on tweet stance classification employs the same technique but uses Word2Vec instead of the WordNet thesaurus to replace words in text. Synonyms of a given word are ranked based on cosine similarity between the Word2Vec vector of given word and that of each synonym.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural language model based training data augmentation for weakly supervised early rumor detection

Han

Gao

Ciravegna

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment limited labeled rumor source tweets. This work is based on rumor spreading patterns revealed by recent rumor studies and semantic relatedness between labeled and unlabeled data. A state-ofthe-art neural language model (NLM) and large credibilityfocused Twitter corpora are employed to learn context-sensitive representations of rumor tweets. Six different real-world events based on three publicly available rumor datasets are employed in our experiments to provide a comparative evaluation of the effectiveness of the method. The results show that our method can expand the size of an existing rumor data set nearly by 200% and corresponding social context (i.e., conversational threads) by 100% with reasonable quality. Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs). With augmented data, the performance of rumor detection can be improved by 12.1% in terms of F-score. Our experiments also indicate that augmented training data can help to generalize rumor detection models on unseen rumors.

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Section: A Overview Of the Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural language model based training data augmentation for weakly supervised early rumor detection

Han

Gao

Ciravegna

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…While Shenoy et al [51] create a target specific bag of POS-tagged keywords along with sentiments, Patra et al [40] create target-specific topic bags that include dependency information among lexicons and hashtags to detect stance in tweets. Various machine learning frameworks, like an ensemble of classifiers [30] and character level and word level CNN [61] have been proposed. However, most of these existing approaches fail to detect stance related to multiple targets.…”

Section: 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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“…Zhang and LeCun (2015) use a thesaurus to generate new training examples based on synonyms. Vijayaraghavan et al (2016) employs a similar method, but uses Word2vec and cosine similarity to find similar words. Jia and Liang (2016) use a high-precision synchronous context-free grammar to generate new semantic parsing examples.…”

Section: Related Workmentioning

confidence: 99%

Globally Normalized Reader

Raiman

Miller

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Rapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bidirectional attention mechanisms or score all possible answer spans, limiting scalability. We propose instead to cast extractive QA as an iterative search problem: select the answer's sentence, start word, and end word. This representation reduces the space of each search step and allows computation to be conditionally allocated to promising search paths. We show that globally normalizing the decision process and back-propagating through beam search makes this representation viable and learning efficient. We empirically demonstrate the benefits of this approach using our model, Globally Normalized Reader (GNR), which achieves the second highest single model performance on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow. We also introduce a data-augmentation method to produce semantically valid examples by aligning named entities to a knowledge base and swapping them with new entities of the same type. This method improves the performance of all models considered in this work and is of independent interest for a variety of NLP tasks.

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DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs

Cited by 8 publications

References 0 publications

Neural language model based training data augmentation for weakly supervised early rumor detection

Neural language model based training data augmentation for weakly supervised early rumor detection

Detecting Stance in Tweets : A Signed Network based Approach

Globally Normalized Reader

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