Abstract

Hyperlipidemia is a non-communicable disease (NCD) caused by several factors, such as a person's socioeconomic status, culture, customs, habits, and lifestyle. Through user interaction on social media, we can discover the model anti-hyperlipidemia by extracting information, complaints, suggestions, and calls for help about the treatment, which will play a role as an intervention to reduce hyperlipidemia in Indonesia. This study aimed to identify factors influencing perceptions of hyperlipidemia drugs and resulting sentiment on the social media platform Twitter. This study used user-uploaded tweet data to compare perceptions of hyperlipidemia drugs in 2020 and keywords for hyperlipidemia terms and medicine. Tweets related to anti-hyperlipidemia were extracted by issuing tweets containing advertisements, news, re-tweet, and content outside of health. The tweet data obtained was then carried out through content analysis, including point of view, theme, and sentiment analysis, to identify whether the resulting tweets are positive, neutral, or negative using the Support Vector Machine (SVM) method. We identified 1572 hyperlipidemia-related tweets and 153 specific tweets describing hyperlipidemia medications. Tweets about anti-hyperlipidemia showed 99 tweets from the first-person perspective, 23 from the second-person perspective, 22 from healthcare professionals, and nine unidentifiable (other). Sixty-three tweets talked about the benefits of lipid-lowering drugs, 17 complaint tweets, 49 suggestion tweets, 17 question tweets, and two side effect tweets. Assessing public perceptions and sentiment toward hyperlipidemia treatment can be used to develop strategies to increase treatment adherence, improve treatment outcomes, and target health promotion efforts.

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