Twitter trends in #Parasitology determined by text mining and topic modelling

Area of Science:

• Parasitology
• Social Media Analysis
• Computational Linguistics

Background:

• Twitter (now X) has become a significant platform for scientific communication.
• Understanding its role in parasitology is crucial for tracking research trends and public discourse.
• Previous analyses of social media in scientific fields are limited.

Purpose of the Study:

• To investigate the emergence and usage patterns of Twitter for communication in parasitology.
• To analyze the content, language, topics, and sentiment of parasitology-related tweets.
• To identify key stakeholders and assess information quality on the platform.

Main Methods:

• Collected a dataset of tweets using keywords ('malaria', 'Plasmodium', 'Leishmania', 'Trypanosoma', 'Toxoplasma', 'Schistosoma') from 2011-2020.
• Performed exploratory data analysis on tweet content (language, usernames, hashtags).
• Extracted discussion topics using KeyBert and biterm topic modeling.
• Analyzed tweet sentiment using VADER (Valence Aware Dictionary and sEntiment Reasoner).

Main Results:

• Tweet volume for all investigated parasites increased significantly from 2011/2013 to 2020, peaking in 2020.
• English was the primary language, but Spanish and Portuguese were also prevalent, especially for Leishmania.
• Topics included mental health links for Toxoplasma, drugs/vectors for Trypanosoma, and pathology/snails for Schistosoma.
• Trusted sources like journals and societies were active, but non-expert influencers also contributed, raising quality concerns.

Conclusions:

• Twitter has become a major communication forum for parasitology research and discussion.
• The platform facilitates diverse conversations, but the rise of non-expert voices necessitates critical evaluation of information.
• Further research into the impact of social media on scientific understanding and misinformation in parasitology is warranted.