The richness of social media data has opened a new avenue for social science research to gain insights into human behaviors and experiences. In particular, emerging data-driven approaches relying on topic models provide entirely new perspectives on interpreting social phenomena. However, the short, text-heavy, and unstructured nature of social media content often leads to methodological challenges in both data collection and analysis. In order to bridge the developing field of computational science and empirical social research, this study aims to evaluate the performance of four topic modeling techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. In view of the interplay between human relations and digital media, this research takes Twitter posts as the reference point and assesses the performance of different algorithms concerning their strengths and weaknesses in a social science context. Based on certain details during the analytical procedures and on quality issues, this research sheds light on the efficacy of using BERTopic and NMF to analyze Twitter data.

Focusing on the application of artificial intelligence, this study investigates the impact of emotional display on user engagement with computer-generated imagery influencers through the lens of the computers are social actors (CASA) framework. It breaks down emotions into individual muscle movements (i.e., facial action units). By using facial recognition based on 1,028 pictures shared by Lil Miquela, the findings disclose the significance of happiness, sadness, disgust, and surprise in triggering user engagement when promoting diverse products with visually captivating content. The findings highlight the importance of balancing the intensity of muscle movement to streamline the interplay between technology, human behaviour, and digital communication.

Contemporary consumer behavior is characterized by its multidimensionality and complexity, which, at the same time, pushes traditional segmentation approaches to their limits. In response, this methodological study proposes a multistage machine learning-based segmentation process using semiotic-semantic community detection. This innovative method was conducted exemplarily and evaluated on a representative sample of 1,101 German travelers. The main contribution of this study lies in the novel use of word vectors, which result from assigning a semiotic meaning to travel-type images. Thus, high-dimensional data could be used during the segmentation process, overcoming several classical segmentation problems. By using semantic similarities, tourists could be grouped and represented in their multidimensionality. From a theoretical perspective, this study was inspired by postmodern tourism practices in …

Symbols are powerful in branding and marketing to represent tourist attractions. By bridging semiotics, marketing, and data science in the tourism context, this study uncovers the destination image based on Instagram photographs. This study constructed a novel methodological framework by evaluating different machine learning models to group textual information based on pictorial content. The results highlighted specific destination image clusters such as the wilderness and spirituality of alpine experiences. This information facilitates marketers’ understanding of tourists’ preferences and movement. It also discloses blind spots that are less promoted by the marketers.

Color plays a critical role in recognizing tourist experiences and influencing their emotions. By classifying tourism photos on Instagram using machine learning, this study uncovers the relationship between color and user engagement based on pictures with different features. The findings show that the presence of the color blue in photos featuring natural scenery, high-end gastronomy, and sacral architectures contributes to user engagement. A red/orange color scheme enhances pictures regarding local delicacies and ambience, while the coexistence of violet and warm colors is crucial for photographs featuring cityscapes and interior design. By taking a broader lens from aesthetic philosophy and narrowing down to color psychology, this study offers guidelines for marketers to promote tourism activities through the application of color.

Given the rich content that foodstagrammers, people who actively share their dining experiences using photographs and texts on social media, post, they considerably shape a destination’s gastronomic image. Using big data analytics, this study examined the formation of gastronomic images from foodstagrammers’ perspectives and the associated emotions. Moreover, it demonstrated the applicability of the proposed machine learning approach to evaluate both textual and pictorial content on social media. The study findings extend the current understanding of gastronomic images by identifying the underlying attributes based on the interplay of the three dimensions of food, environment, and activities. Furthermore, the results reveal specific image clusters and dimensions that arouse positive sentiments among foodstagrammers and influence users’ engagement with the post. For practitioners, this study provides …