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

In a recently published study in PLUS ONEresearchers analyzed disinformation about coronavirus disease 2019 (COVID-19) on Twitter.

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


The widespread use of social media during the COVID-19 pandemic had led to an “infodemic” of dis- and misinformation related to COVID-19, with potentially fatal consequences. Understanding the magnitude and impact of this misinformation is critical for public health officials to assess the behavior of the general population in relation to vaccine uptake and non-pharmaceutical interventions (NPIs) such as social distancing and masking.

About the study

In the present study, the researchers rated tweets circulating on Twitter with 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 that were posted between January 1 and December 31, 2020. On January 15, 2021, the team then deployed the Twitter application programming software (API) to receive the same tweets using corresponding tweet identities. The team provided descriptive statistics for the selected tweets, such as: B. The correlating content of the tweet and the user profiles to determine the availability of the tweets in both sets of data developed according to the Twitter API status codes.

Sentiment analysis of the tweets was performed by tokenizing and sanitizing the tweets. The tokens were then converted to their stemmed form using natural language processing techniques including lemmatization, stemming, and stop word removal. Python’s VADER library was used to detect and categorize the tweet’s sentiment as either neutral, positive, or negative and the tweet’s subjectivity as either subjective or objective. VADER applied a rule-based analysis of moods with a polarity scale between -1 and 1.

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

The predominant topics discussed in the tweet library were detected and a machine learning algorithm was applied. This algorithm identified the tweet clusters using a representative set of words. The words with the highest weights in each cluster were used to define the content of each topic.


The study 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 keep 227,067 tweets from approximately 40,081 users. Almost 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 suspended until January 15, 2021. The team found that users with banned profiles were more likely to tweet. Users who used both hashtags had a 29.2% chance of being blocked, versus 25.9% for tweets containing #Plandemic and 13.2% for tweets containing #Scamdemic.

The team found that most users were 40 years and older. Furthermore, the banned users were mostly males and users aged 18 and under and 30-39 years old. Almost 88% of active users and 79% of blocked users tweeted from their personal accounts. Remarkably, almost 65% of the analyzed tweets showed objectivity.

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

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

The most-watched tweet topic was “complaints against mandates introduced during the COVID-19 pandemic,” including complaints about face masks, closures, and social distancing. This was followed by tweets on the topics of “downplaying the dangers of COVID-19”, “lies and brainwashing by politicians and the media”, and “corporations and the global agenda”.

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

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