Fake News Analysis Modeling Using Quote Retweet

Jang, Yonghun; Park, Chang-Hyeon; Seo, Yeong-Seok

doi:10.3390/electronics8121377

Cited by 33 publications

(20 citation statements)

References 48 publications

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“…The image processing component involves BGR to RGB conversion, resizing images (to [300, 300, 3]), the feature extraction with ResNet50 with a linear layer projecting pre-trained embeddings to 512 dimensional vector. The input and output dimensions of two FC layers for text only models are [300, 300] and [300,3] respectively. For text+image model, the hidden layers dimensions are [300, 300] and [300, 3] respectively.…”

Section: Hypothesis Only Testmentioning

confidence: 99%

“…Prior efforts focus mostly on text from news media articles and English language. In recent years, with the advance in user-generated content and increasingly polarized social platforms, the challenges of fact checking has increasingly become multilingual and multimodal which have seen pervasive in user-generated multimedia content [3]. As a consequence, many new problems arise, typically false context, false connections or misleading content [4,5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

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Logically at Factify 2022: Multimodal Fact Verification

Gao¹,

Hoffmann²,

Oikonomou³

et al. 2021

Preprint

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This paper describes our participant system for the multi-modal fact verification (Factify) challenge at AAAI 2022. Despite the recent advance in text based verification techniques and large pre-trained multimodal models cross vision and language, very limited work has been done in applying multimodal techniques to automate fact checking process, particularly considering the increasing prevalence of claims and fake news about images and videos on social media. In our work, the challenge is treated as multimodal entailment task and framed as multi-class classification. Two baseline approaches are proposed and explored including an ensemble model (combining two uni-modal models) and a multimodal attention network (modeling the interaction between image and text pair from claim and evidence document). We conduct several experiments investigating and benchmarking different SoTA pre-trained transformers and vision models in this work. Our best model is ranked first in leaderboard which obtains a weighted average F-measure of 0.77 on both validation and test set. Exploratory analysis of dataset is also carried out on the Factify data set and uncovers salient patterns and issues (e.g., word overlapping, visual entailment correlation, source bias) that motivates our hypothesis. Finally, we highlight challenges of the task and multimodal dataset for future research.

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Section: Hypothesis Only Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Logically at Factify 2022: Multimodal Fact Verification

Gao¹,

Hoffmann²,

Oikonomou³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A statistical analysis was performed on the experimental results to objectively assess the proposed method. An independent sample t-test is often used to compare the population means of two groups, mainly to observe the similarities or differences between two different test groups [75][76][77][78]. In our experiment, an independent sample t-test was used to determine if there was a significant difference between the high-quality and synthesized images.…”

Section: 𝑖𝑛𝑡𝑟𝑎 𝑐𝑙𝑎𝑠𝑠 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 = 𝛼𝜎 + 𝛽𝜎mentioning

confidence: 99%

Intelligent Image Synthesis for Accurate Retinal Diagnosis

2020

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Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using the multilayer perceptron concept with artificial neural networks. The approach proposed in this study can convert vessel images to vessel-centered images with clearer identification, even for low-resolution retinal images. To verify the proposed approach, we determined whether high-resolution vessel images could be extracted from low-resolution images through a statistical analysis using high- and low-resolution images extracted from the same patient.

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“…Misinformation can be of several types: (1) it can be completely inaccurate, (2) it can be a belief disseminated without any authentic proof, or (3) it can carry biased information where selective facts are shared to achieve some mischief propaganda [5][6][7]. The study [8] pointed out that unreliable content on social media spread way faster than fact-based content.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the article also presents a stance detection system to combat the problem of fake news on the social network. Stance classification aims to determine whether the author of the text stands in favor or against a news title [7]. Stance detection has been considered a vital task for solving many problems, including fake news detection [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

2020

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The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain.

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Fake News Analysis Modeling Using Quote Retweet

Cited by 33 publications

References 48 publications

Logically at Factify 2022: Multimodal Fact Verification

Logically at Factify 2022: Multimodal Fact Verification

Intelligent Image Synthesis for Accurate Retinal Diagnosis

Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

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