Stance Prediction Research Articles

Stance prediction with a relevance attribute to political issues in comparing the opinions of citizens and city councilors

Stance prediction with a relevance attribute to political issues in comparing the opinions of citizens and city councilors

Enhancing prediction of user stance for social networks rumors

The spread of social media has led to a massive change in the way information is dispersed. It provides organizations and individuals wider opportunities of collaboration. But it also causes an emergence of malicious users and attention seekers to spread rumors and fake news. Understanding user stances in rumor posts is very important to identify the veracity of the underlying content as news becomes viral in a few seconds which can lead to mass panic and confusion. In this paper, different machine learning techniques were utilized to enhance the user stance prediction through a conversation thread towards a given rumor on Twitter platform. We utilized both conversation thread features as well as features related to users who participated in this conversation, in order to predict the users’ stances, in terms of supporting, denying, querying, or commenting (SDQC), towards the source tweet. Furthermore, different datasets for the stance-prediction task were explored to handle the data imbalance problem and data augmentation for minority classes was applied to enhance the results. The proposed framework outperforms the state-of-the-art results with macro F1-score of 0.7233.

FLACORM: fuzzy logic and ant colony optimization for rumor mitigation through stance prediction in online social networks

FLACORM: fuzzy logic and ant colony optimization for rumor mitigation through stance prediction in online social networks

Heterogeneous Graph Contrastive Learning for Stance Prediction

Heterogeneous Graph Contrastive Learning for Stance Prediction

Stance Prediction of Tweets on Farmers Protests in India

Abstract: Protests are an integral part of democracy and are a vital tool for the general public to convey their demands and/or discontentment to the ruling government. As voters return to term with any new rules, there are an increasing range of protests everywhere in the world for numerous socio-political reasons. With the advancement of technology, there has additionally been an exponential rise within the use of social media for the exchange of data and ideas. During this research, knowledge was gathered from the web site “twitter.com”, regarding farmers’ protest to know the feelings that the public shared on a global level. Sadly now since the Farm Laws are repealed, we have a tendency to aim to use this knowledge to know the general public stance on these laws, and whether or not it affected the government’s decision. This paper proposes a stance prediction deep learning model achieved after fine tuning the well known ULMFiT (Universal Language Model Fine-tuning) model by Howard and Ruder. Categories to be classified into are For (F), Against (A) and Neutral (N). Proposed model achieved an F1 score of 0.67 on our training and test data, which is essentially a labeled subset of the actual data. Keywords: Dataset, ULMFiT, deep learning, text classification, Language Model (LM)

A Survey Paper on Stance Detection of Tweets on Farmers Protests in India

Abstract: Protests are a critical part of democracy and a crucial medium for people to deliver their needs and/or dissatisfaction to the authorities. As farmers felt a threat to their rights, there were more and more protests all around the nation. With the development of this technology, additionally there has been a sudden rise in the use of social network sites to trade facts and ideas. In this study, we collected information from the social networking internet site Twitter regarding the Farmers’ protest to apprehend the feelings that the twitter users shared on a worldwide platform. In the midst of this protest, social media users had been very lively in voicing their opinion about the matter using the "#FarmersProtest". With lots of people tweeting with the hashtag daily. Through the Stance Prediction of over 850,000 tweets and over 150,000 Users, We intend to decide the Inclination of Common Citizen in addition to Influential People on the now repealed Laws. Keywords: Stance Detection, ULMFIT, Language Model, Farmers, Twitter

Affection Enhanced Relational Graph Attention Network for Sarcasm Detection

Sarcasm detection remains a challenge for numerous Natural Language Processing (NLP) tasks, such as sentiment classification or stance prediction. Existing sarcasm detection studies attempt to capture the subtle semantic incongruity patterns by using contextual information and graph information through Graph Convolutional Networks (GCN). However, direct application of dependence may inevitably introduce noisy information and inferiorly in modeling long-distance or disconnected words in the dependency tree. To better learn the sentiment inconsistencies between terms, we propose an Affection Enhanced Relational Graph Attention network (ARGAT) by jointly considering the affective information and the dependency information. Specifically, we use Relational Graph Attention Networks (RGAT) to integrate relation information guided by a trainable matrix of relation types and synchronously use GCNs to integrate affection information explicitly donated by affective adjacency matrixes. The employment of RGAT contributes to information interaction of structural relevant word pairs with a long distance. With the enhancement of affective information, the proposed model can capture complex forms of sarcastic expressions. Experimental results on six benchmark datasets show that our proposed approach outperforms state-of-the-art sarcasm detection methods. The best-improved results of accuracy and F1 are 4.19% and 4.33%, respectively.

HARGAN: Heterogeneous Argument Attention Network for Persuasiveness Prediction

Argument structure elaborates the relation among claims and premises. Previous works in persuasiveness prediction do not consider this relation in their architectures. To take argument structure information into account, this paper proposes an approach to persuasiveness prediction with a novel graph-based neural network model, called heterogeneous argument attention network (HARGAN). By jointly training on the persuasiveness and stance of the replies, our model achieves the state-of-the-art performance on the ChangeMyView (CMV) dataset for the persuasiveness prediction task. Experimental results show that the graph setting enables our model to aggregate information across multiple paragraphs effectively. In the meanwhile, our stance prediction auxiliary task enables our model to identify the viewpoint of each party, and helps our model perform better on the persuasiveness prediction.

Monitoring stance towards vaccination in twitter messages

BackgroundWe developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. At the moment, such monitoring is done by means of regular sentiment analysis with a poor performance on detecting negative stance towards vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms.ResultsWe found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, considerably outperforming the currently used sentiment analysis that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. We also show that the recall of our system could be optimized to 0.60 at little loss of precision.ConclusionThe outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Nonetheless, the model showed sufficient recall on identifying negative tweets so as to reduce the manual effort of reviewing messages. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions.

Neural opinion dynamics model for the prediction of user-level stance dynamics

Social media platforms allow users to express their opinions towards various topics online. Oftentimes, users’ opinions are not static, but might be changed over time due to the influences from their neighbors in social networks or updated based on arguments encountered that undermine their beliefs. In this paper, we propose to use a Recurrent Neural Network (RNN) to model each user’s posting behaviors on Twitter and incorporate their neighbors’ topic-associated context as attention signals using an attention mechanism for user-level stance prediction. Moreover, our proposed model operates in an online setting in that its parameters are continuously updated with the Twitter stream data and can be used to predict user’s topic-dependent stance. Detailed evaluation on two Twitter datasets, related to Brexit and US General Election, justifies the superior performance of our neural opinion dynamics model over both static and dynamic alternatives for user-level stance prediction.

An intelligent monitoring system for indoor safety of individuals suffering from Autism Spectrum Disorder (ASD)

Autism Spectrum Disorder (ASD) is distinguished by a variety of behavioral and social deficits. One of the most prominent problems in an autistic child is their aggressive nature that can cause physical injuries. In order to address the following behavioral deficits, this paper is an attempt to present a novel monitoring framework to predict irregularities from the physical activities of an autistic child. The primary motive behind this study is to improve the safety measures for the child in an indoor environment by generating time sensitive-alerts for caretakers and doctors. In this study, a deep 3D CNN and LSTM based activity prediction methodology are proposed to recognize physical irregularities. The 3D CNN model extracts the spatio-temporal features from the video templates for stance prediction with subject position. The LSTM model calculates the temporal relationship in the feature maps to analyze the scale of irregularity. Moreover, in order to deal with such irregularities, a time-sensitive alert-based decision process is proposed in the present work to generate early warnings to the doctor and caretaker. The proficiency of the system is increased by storing the performed activity scores in the local database of the system which can be further utilized to provide medicinal or therapeutic assistance. In addition, the measured experimental results are validated with state-of-the-art methodologies. Hence, the calculated statistical measures justify the utility of the proposed study in the healthcare and assistive-care domain.

Unsupervised Model for Topic Viewpoint Discovery in Online Debates Leveraging Author Interactions

Online debate forums provide a valuable resource for textual discussions about controversial social and political issues. Discovering the viewpoints and their discourse or arguments from such resources is important for policy and decision makers. In order to detect the stance, most of the existing methods rely on expensively obtained human annotations and propose supervised solutions. In this work, we introduce a purely unsupervised Author Interaction Topic Viewpoint model (AITV) for viewpoint identification at the post and the discourse levels. The model favors "heterophily" over "homophily" when encoding the nature of the authors' interactions in online debates. It assumes that the difference in viewpoints breeds interactions, unlike similar studies based on social network analysis, which hypothesize that similar viewpoints encourage interactions. We evaluate the model's viewpoint identification and clustering accuracies at the author and post levels. Experiments are held on six corpora about four different controversial issues, extracted from two online debate forums. AITV's results show a better performance in terms of viewpoint identification at the post level than the state-of-the-art supervised methods in terms of stance prediction, even though it is unsupervised. It also outperforms a recently proposed topic model for viewpoint discovery in social networks and achieves close results to a weakly guided unsupervised method in terms of author level viewpoint identification. Our results highlight the importance of encoding "heterophily" for purely unsupervised viewpoint identification in the context of online debates. We also carry out a brief qualitative evaluation of the discourse modeling in terms of Topic-Viewpoint word clusters. AITV shows encouraging results suggesting an accurate discovery of the viewpoints and topics' discourses.