Study: Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study. Image Credit: rafapress/Shutterstock

#Plandemic and #Scamdemic tweets during the COVID-19 pandemic

In a recent study published in PLOS ONEresearchers analyzed misinformation about 2019 coronavirus disease (COVID-19) on Twitter.

Study: Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study† Image credit: rafapress/Shutterstock

Background

The widespread use of social media during the COVID-19 pandemic had led to an ‘infodemic’ of misinformation and misinformation about COVID-19, with potentially fatal consequences. Understanding the magnitude and impact of this false information is essential for public health agencies to assess the behavior of the general population regarding vaccine uptake and non-pharmaceutical interventions (NPIs) such as social distancing and masking.

About the study

In the current study, researchers rated tweets circulating on Twitter using the hashtags #Plandemic and #Scamdemic.

On January 3, 2021, the team used Twint, a Twitter scraping tool, to collect English-language tweets with the hashtags #Plandemic or #Scamdemic posted between January 1 and December 31, 2020. On January 15, 2021, the team subsequently launched the Twitter application. programming software (API) to obtain the same tweets using corresponding tweet identities. The team provided descriptive statistics for the selected tweets, such as the correlating content of the tweet and user profiles, to determine the availability of the tweets in both datasets developed according to the Twitter API status codes.

Sentiment analysis of the tweets was performed by tokenizing and cleaning the tweets. The tokens were then converted to their root form using natural language processing techniques, including lemmatization, stem, and stopword removal. Python’s VADER library was used to recognize and categorize the sentiment of the tweet as neutral, positive, or negative and the subjectivity of the tweet as subjective or objective. VADER applied a rules-based analysis of sentiments with a polarity scale between -1 and 1.

The subjective analysis was performed using TextBlob, which labeled each tweet on a scale from zero or objective to one or subjective. Objective tweets were considered facts, while subjective tweets communicated an opinion or a belief. The team visualized a histogram of the subjectivity scores for the hashtags #Plandemic and #Scamdemic. The Python library was also used to label the primary emotion associated with each tweet as fear, anticipation, anger, surprise, confidence, sadness, joy, disgust, positive or negative.

The key topics discussed in the tweet library were recognized and a machine learning algorithm was applied. This algorithm identified the clusters of tweets using a representative group of words. The words with the highest weight in each cluster were used to define the content of each topic.

Results

The survey results showed that a total of 420,107 tweets contained the hashtags #Plandemic and #Scamdemic. The team removed tweets that were retweets, replies, non-English, or duplicates to retain 227,067 tweets from approximately 40,081 users. Nearly 74.4% of total tweets were posted by 78.4% of active Twitter users, while 25.6% of tweets were posted by 21.6% of users whose account was January 15, 2021 suspended. The team noted that users with suspended profiles are more likely to tweet. Users using both hashtags had a 29.2% chance of being suspended, as opposed to 25.9% for #Plandemic tweets and 13.2% for #Scamdemic tweets.

The team found that most users were 40 and older. In addition, the suspended users were mainly males and users aged 18 and under and 30 to 39 years of age. Nearly 88% of active users and 79% of suspended users tweeted from their personal accounts. Notably, objectivity was shown by nearly 65% ​​of the tweets analyzed.

Emotion analysis of the tweets revealed that fear was the predominant emotion, followed by sadness, confidence and anger. Emotions such as surprise, disgust and joy were the least expressed, while suspended tweets were more likely to show disgust, surprise and anger.

The general sentiment expressed by the tweets with #Plandemic and #Scamdemic hashtags was negative. Overall average weekly sentiments were -0.05 for #Plandemic and -0.09 for #Scamdemic, with 1 and -1 indicating fully positive and negative sentiments, respectively.

The most frequently observed tweet topic was “complaints against mandates filed during the COVID-19 pandemic,” which also included complaints about facemasks, closures and social distancing. This was followed by tweets with topics of ‘downplaying the dangers of COVID-19’, ‘lies and brainwashing by politicians and the media’ and ‘business and global agenda’.

Overall, the survey results showed that the COVID-19-related tweets showed overall negative sentiment. While several tweets expressed anger at the restrictions during the pandemic, a significant portion of the tweets also presented disinformation.

#Plandemic #Scamdemic #tweets #COVID19 #pandemic

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