OBJECTIVES: This study aimed to analyze tweets on Twitter related to cancer prevention and screening in Italy. The objective was to understand the characteristics of the web source disseminating cancer prevention information and examine user engagement with these tweets.

METHODS: Python libraries were utilized to analyze more than 23,500 Italian tweets from 2017 to 2022. The analysis focused on cancer prevention and screening tweets, extracted using specific keywords and hashtags. Relevant information such as tweet ID, author details, creation date, text, and engagement metrics (likes, retweets, replies) was extracted from the CSV dataset. Data cleaning techniques were applied to remove null or duplicate text, emoticons, web links, tags, and stop words. Engagement scores based on tweet content were calculated using filters and regular expressions.

RESULTS: Among the 13,735 analyzed tweets, 57% were related to colon/rectum, breast, prostate, or skin cancer. Breast cancer tweets comprised 45% of the total and received the highest engagement, with 49% likes, 41% retweets, and 60% replies. The colon/rectum and prostate categories had similar engagement scores but fewer tweets (803 and 534, respectively). Likes for colon/rectum remained consistent, peaking in 2021, while breast cancer likes were stable with peaks in 2018 and 2020. The study also identified the top five authors with the most published tweets.

CONCLUSIONS: The analysis revealed that breast cancer-related tweets received the most engagement in terms of likes, retweets, and replies. Engagement varied over time across different types of cancer. The study proposes incorporating author information and sentiment analysis could enhance the understanding of influence and engagement. Enriching the dataset with more author details could facilitate sentiment analysis and the development of generative models for relevance and engagement. This research offers insights into how cancer-related information spreads on Twitter and user engagement trends.