A Dataset for Multi-Target Stance Detection

Sobhani, Parinaz; Inkpen, Diana; Zhu, Xiaodan

doi:10.18653/v1/e17-2088

Cited by 88 publications

(68 citation statements)

References 18 publications

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“…By considering the dependency of related targets, Sobhani et al [5] introduced a multi-target stance detection (MTSD) task and proposed an attentive encoder-decoder network to capture the dependencies among stance labels regarding multiple targets. Later, Wei et al [23] proposed a dynamic memory-augmented network that utilized a shared external memory to capture and store multi-targets stance indicative clues dynamically.…”

Section: Related Workmentioning

confidence: 99%

“…At first, we utilize the multi-kernel convolution filters with four different kernel sizes including [2,3,4,5] to extract the higher-level feature sequences from the target appended tweet embeddings. The generated feature sequences are then concatenated and fed into the densely connected Bi-LSTM and nested LSTMs to learn long-term dependencies.…”

Section: Our Proposed Stance Detection Frameworkmentioning

confidence: 99%

“…We evaluate our proposed stance detection framework on two benchmark twitter datasets: single target stance detection dataset used in SemEval-2016 Task 6-A [1] and multitarget stance detection dataset provided by Sobhani et al [5]. At first, we describe the details of our model's configuration and dataset preprocessing task in the next two subsections.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…Here, the stance of the tweet is in favor of Donald Trump while against towards the Hillary Clinton. By considering such scenarios, Sobhani et al [5] introduced a multi-target stance detection (MTSD) task to address the plausible dependency of related targets.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Siddiqua

Chy

Aono

2019

IEICE Trans. Inf. & Syst.

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Umme Aymun SIDDIQUA †a) , Abu Nowshed CHY †b) , Nonmembers, and Masaki AONO †c) , Member SUMMARY Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. Detecting and analyzing user stances from massive opinion-oriented twitter posts provide enormous opportunities to journalists, governments, companies, and other organizations. Most of the prior studies have explored the traditional deep learning models, e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) for detecting stance in tweets. However, compared to these traditional approaches, recently proposed densely connected bidirectional LSTM and nested LSTMs architectures effectively address the vanishinggradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural network model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higherlevel tweet representations. Results of extensive experiments on single and multi-target benchmark stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.

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Section: Related Workmentioning

confidence: 99%

Section: Our Proposed Stance Detection Frameworkmentioning

confidence: 99%

Section: Experiments and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Siddiqua

Chy

Aono

2019

IEICE Trans. Inf. & Syst.

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“…We created the first data set of tweets labeled for more than one target per post for stance. The construction of the data set is described in the work of Sobhani et al, where we present preliminary experiments to show the usefulness of the introduced data set, whereas this paper is focused more on the learning methods and experiments. We substantially extended the previous paper by adding new approaches for multitarget stance classification (Section 3) and by adding new experiments (Section 4).…”

Section: Introductionmentioning

confidence: 99%

Exploring deep neural networks for multitarget stance detection

Sobhani

Inkpen

Zhu

2018

Computational Intelligence

Self Cite

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Detecting subjectivity expressed toward concerned targets is an interesting problem and has received intensive study. Previous work often treated each target independently, ignoring the potential (sometimes very strong) dependency that could exist among targets (eg, the subjectivity expressed toward two products or two political candidates in an election). In this paper, we relieve such an independence assumption in order to jointly model the subjectivity expressed toward multiple targets. We propose and show that an attention‐based encoder‐decoder framework is very effective for this problem, outperforming several alternatives that jointly learn dependent subjectivity through cascading classification or multitask learning, as well as models that independently predict subjectivity toward individual targets.

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Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

Li,

Yang,

Wang

et al. 2024

Concurrency and Computation

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SummaryThis research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain‐like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field.

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A Dataset for Multi-Target Stance Detection

Cited by 88 publications

References 18 publications

Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Tweet Stance Detection Using Multi-Kernel Convolution and Attentive LSTM Variants

Exploring deep neural networks for multitarget stance detection

Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

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