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Register a new userBoth Rates of Fake News and Fact-based News on Twitter Negatively Correlate with the State-level COVID-19 Vaccine Uptake
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Added by
Hanjia Lyu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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There is evidence of misinformation in the online discourses and discussions about the COVID-19 vaccines. Using a sample of 1.6 million geotagged English tweets and the data from the CDC COVID Data Tracker, we conduct a quantitative study to understand the influence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the U.S. from April 19 when U.S. adults were vaccine eligible to May 7, 2021, after controlling state-level factors such as demographics, education, and the pandemic severity. We identify the tweets related to either misinformation or fact-based news by analyzing the URLs. By analyzing the content of the most frequent tweets of these two groups, we find that their structures are similar, making it difficult for Twitter users to distinguish one from another by reading the text alone. The users who spread both fake news and fact-based news tend to show a negative attitude towards the vaccines. We further conduct the Fama-MacBeth regression with the Newey-West adjustment to examine the effect of fake-news-related and fact-related tweets on the vaccination rate, and find marginally negative correlations.
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COVID-19 has impacted all lives. To maintain social distancing and avoiding exposure, works and lives have gradually moved online. Under this trend, social media usage to obtain COVID-19 news has increased. Also, misinformation on COVID-19 is frequently spread on social media. In this work, we develop CHECKED, the first Chinese dataset on COVID-19 misinformation. CHECKED provides a total 2,104 verified microblogs related to COVID-19 from December 2019 to August 2020, identified by using a specific list of keywords. Correspondingly, CHECKED includes 1,868,175 reposts, 1,185,702 comments, and 56,852,736 likes that reveal how these verified microblogs are spread and reacted on Weibo. The dataset contains a rich set of multimedia information for each microblog including ground-truth label, textual, visual, temporal, and network information. Extensive experiments have been conducted to analyze CHECKED data and to provide benchmark results for well-established methods when predicting fake news using CHECKED. We hope that CHECKED can facilitate studies that target misinformation on coronavirus. The dataset is available at https://github.com/cyang03/CHECKED.
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information. Considering the problematic consequences that the COVID-19 fake-news have brought, the scientific community has put effort to tackle it. To contribute to this fight against the infodemic, we aim to achieve a robust model for the COVID-19 fake-news detection task proposed at CONSTRAINT 2021 (FakeNews-19) by taking two separate approaches: 1) fine-tuning transformers based language models with robust loss functions and 2) removing harmful training instances through influence calculation. We further evaluate the robustness of our models by evaluating on different COVID-19 misinformation test set (Tweets-19) to understand model generalization ability. With the first approach, we achieve 98.13% for weighted F1 score (W-F1) for the shared task, whereas 38.18% W-F1 on the Tweets-19 highest. On the contrary, by performing influence data cleansing, our model with 99% cleansing percentage can achieve 54.33% W-F1 score on Tweets-19 with a trade-off. By evaluating our models on two COVID-19 fake-news test sets, we suggest the importance of model generalization ability in this task to step forward to tackle the COVID-19 fake-news problem in online social media platforms.
The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user claims about particular COVID-19 claims. The second model verifies the level of truth in the claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.
The advent of social media changed the way we consume content favoring a disintermediated access and production. This scenario has been matter of critical discussion about its impact on society. Magnified in the case of Arab Spring or heavily criticized in the Brexit and 2016 U.S. elections. In this work we explore information consumption on Twitter during the last European electoral campaign by analyzing the interaction patterns of official news sources, fake news sources, politicians, people from the showbiz and many others. We extensively explore interactions among different classes of accounts in the months preceding the last European elections, held between 23rd and 26th of May, 2019. We collected almost 400,000 tweets posted by 863 accounts having different roles in the public society. Through a thorough quantitative analysis we investigate the information flow among them, also exploiting geolocalized information. Accounts show the tendency to confine their interaction within the same class and the debate rarely crosses national borders. Moreover, we do not find any evidence of an organized network of accounts aimed at spreading disinformation. Instead, disinformation outlets are largely ignored by the other actors and hence play a peripheral role in online political discussions.
In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which misleading or even harmful, that has widely spread around the globe. Although Social Media are increasingly being used as information source, Web Search Engines, like Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through Search Engines. By using a real-world dataset associated with recent CoViD-19 pandemic, we first apply re-sampling techniques for class imbalance, then we use existing Machine Learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated Uniform Resource Locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URLs detection, can improve the efficiency and performance of classifiers. Based on these findings, we suggest that the use of both textual and URLs features can improve the effectiveness of fake news detection methods.
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