Treffer: HOW DO FIRMS USE SOCIAL MEDIA: TOPIC MODELING OF TWITTER BRAND POSTS OF FOUR INDONESIAN SKINCARE BRANDS.

Manuscript type: Empirical paper Research Aims: This paper aims to identify the topics of social media posts (on Twitter) from local skincare brands in Indonesia. Design/methodology/approach: This descriptive study applied text analysis methods, specifically frequency analysis and topic modeling with the Latent Dirichlet Allocation (LDA) method. Data were collected from the top four skincare brands with over 10,000 followers using Twitter Academic API interfaced through the Twarc package in Python. The dataset contained 1290 tweets from Wardah, 17,491 tweets from Avoskin, 1,968 tweets from Somethinc, and 590 tweets from Whitelab. The data analysis used quanteda and topicmodels packages from R software. Research findings: The findings revealed that the brands' tweets can be categorised into 8-10 topics, which can be distilled further into three significant functions proposed by Saxton and Waters (2014): information, promotion and mobilisation, and community building and dialogue. Theoretical Contribution/Originality: This study adds to the body of knowledge regarding social media marketing by adding insights into how local Indonesian brands carry out their social media marketing efforts. Practitioner/Policy Implication: The findings implied that marketing managers should emphasise providing timely and relevant information as a base that can be used to build dialogic communication efforts with their audience. Research limitation/Implications: This study only looked at four brands in the local skincare product category. In addition, this study applied automated topic modeling with LDA, which needed an a priori number of topics, which might affect the topic labelling. Furthermore, this study's scope was limited to categorising the brand posts without examining the effects of brand post types on engagement metrics. Future studies can examine more samples by observing other product categories, applying thematic analysis to code each tweet, and comparing the results to topic modeling results. Lastly, future studies can examine the relationship between brand post types/categories and engagement metrics such as likes, comments, and retweets. [ABSTRACT FROM AUTHOR]

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