Characterizing the Online Discourse in Twitter: Users’ Reaction to Misinformation around COVID-19 in Twitter

Kalantari, Niloofar; Liao, Duoduo; Motti, Vivian Genaro

doi:10.1109/bigdata52589.2021.9671740

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…Studies by Cinelli et al [2], Kalantari et al [26] and Giachanou and Rosso [27] have highlighted the role of anonymity and easy access to social networks in the spread and influence of online misinformation, particularly on polarising issues.…”

Section: Online Misinformation Categoriesmentioning

confidence: 99%

Characterizing Discourse and Engagement Across Topics of Misinformation on Twitter

Wojtczak,

Peersman,

Zuccolo

et al. 2023

IEEE Access

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In recent years, online misinformation has become increasingly prevalent, leading to significant issues such as political polarisation and distrust of genuine information. Misinformation on social media platforms affects various aspects of society, including health and politics, and can take many forms, such as text and images. However, current studies mainly focus on analysing singular topics and modalities, without considering the heterogeneity of the issue. Our research aimed to examine the relationship between visual elements and engagement, as well as the relationship between sentiment analysis, hate speech, and bots on a variety of topics on the Twitter social media platform Twitter. We labelled 12,581 misinformation posts that were manually modelled into a topic hierarchy. We then analysed these posts, including their sentiments, the prevalence of hate speech, and bot activity on different topics. The results revealed that political misinformation tends to contain more hate speech than COVID-19 misinformation and that political misinformation also has a higher number of bots. Furthermore, the findings suggest that misinformation online with more than 40% negative sentences can have a high level of hate speech identified for both tweets and replies. This study provides detailed information on topics and the volume of misinformation on social media platforms, and the findings can be used to develop more advanced detection systems and support further analysis. Our findings can help policy makers understand what kind of online misinformation has been spreading on Twitter and how to plan campaigns to make users more aware of how to spot its various features in an online user-to-user Twitter environment.

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Section: Online Misinformation Categoriesmentioning

confidence: 99%

Characterizing Discourse and Engagement Across Topics of Misinformation on Twitter

Wojtczak,

Peersman,

Zuccolo

et al. 2023

IEEE Access

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show abstract

“…Natural language processing (NLP) leverages representation learning approaches across various domains, including text classification [32], question answering [33], machine translation [34]- [36], electronic health records [37], financial forecasting [38], chatbots [39], social media analysis [40], [41] and more. The field of NLP has witnessed an evolution from early rule-based methods to the application of statistical learning techniques, enabled by access to large amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges

Payandeh,

Baghaei,

Fayyazsanavi

et al. 2023

IEEE Access

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Machine learning algorithms have had a profound impact on the field of computer science over the past few decades. The performance of these algorithms heavily depends on the representations derived from the data during the learning process. Successful learning processes aim to produce concise, discrete, meaningful representations that can be effectively applied to various tasks. Recent advancements in deep learning models have proven to be highly effective in capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we provide a comprehensive overview of the current state-of-the-art in deep representation learning and the principles and developments made in the process of representation learning. Our study encompasses both supervised and unsupervised methods, including popular techniques such as autoencoders, self-supervised methods, and deep neural networks. Furthermore, we explore a wide range of applications, including image recognition and natural language processing. In addition, we discuss recent trends, key issues, and open challenges in the field. This survey endeavors to make a significant contribution to the field of deep representation learning, fostering its understanding and facilitating further advancements.

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“…In recent years, Machine Learning, Modeling, and Deep Learning have made significant breakthroughs in numerous fields [34][35][36][37][38]. Computer vision is one of the areas that it affects most successfully.…”

Section: Introductionmentioning

confidence: 99%

Review on 6D Object Pose Estimation With the Focus on Indoor Scene Understanding

Nejatishahidin¹,

Pooya²

2022

AAIML

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6D object pose estimation problem has been extensively studied in the field of Computer Vision and Robotics. It has wide range of applications such as robot manipulation, augmented reality, and 3D scene understanding. With the advent of Deep Learning, many breakthroughs have been made; however, approaches continue to struggle when they encounter unseen instances, new categories, or real-world challenges such as cluttered backgrounds and occlusions. In this study, we will explore the available methods based on input modality, problem formulation, and whether it is a category-level or instance-level approach. As a part of our discussion, we will focus on how 6D object pose estimation can be used for understanding 3D scenes.

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Characterizing the Online Discourse in Twitter: Users’ Reaction to Misinformation around COVID-19 in Twitter

Cited by 11 publications

References 37 publications

Characterizing Discourse and Engagement Across Topics of Misinformation on Twitter

Characterizing Discourse and Engagement Across Topics of Misinformation on Twitter

Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges

Review on 6D Object Pose Estimation With the Focus on Indoor Scene Understanding

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