Purpose: Among the growing interest towards market segmentation and targeted marketing, the current study adopted a scientometric approach to examine the literature on wine selection and preferences. The current review specifically attempts to shed light on the research that explores the determinants of wine preferences at multiple levels of analysis.Design/methodology/approach: CiteSpace was used to compute a Document Co-Citation Analysis (DCA) on a sample of 114,048 eligible references obtained from 2,846 publications downloaded from Scopus on 24 May 2021.Findings: An optimized network of 1,505 nodes and 4,616 links was generated. Within the network, impactful publications on the topic and thematic domains of research were identified. Specifically, two thematic macro-areas were identified through a qualitative analysis of papers included in the 7 major clusters. The first one - “Methods of Wine Making” - included clusters #0, #3, #5, #6 and #18. The second one - “Consumers' Attitudes and Preferences Towards Wine” - included clusters #1 and #2. The first thematic macro-area included more technical aspects referring to the process of wine making, while the second thematic macro-area focused more on the factors influencing individuals' preferences and attitudes towards wine. To reflect the aims of the current paper, publications giving light to the “Consumers' Attitudes and Preferences Towards Wine” macro-area were analyzed in detail.Originality/value: The resulting insights may help wine makers and wine sellers optimize their work in relation to market segments and to the factors influencing individuals' purchasing behaviors.

Social media platforms play a crucial role in shaping public perceptions of neurodevelopmental conditions, such as autism spectrum disorders, by providing spaces for community interaction and content sharing. These platforms hold the potential to foster connections and support among autistic individuals while offering valuable insights into their personal experiences and diverse perspectives. However, knowledge is limited on autism‐related content shared within Reddit, one of the most prominent social media outlets. In this study, we aimed to examine discussions and narratives shared on Reddit about autism, with the dual objectives of identifying the main topics of discussion and exploring the lived experiences of autistic individuals. To achieve this, we utilized state‐of‐the‐art natural language processing techniques to perform a topic modeling analysis on 740,042 autism‐related posts collected from Reddit. Converging themes emerged when comparing the largest and most general subreddit in the dataset ( r/autism ) with 15 additional autism‐related subreddits. The most prominent topics of discussion included challenges in social relationships, behaviors such as stimming, and sensory sensitivities. Additional themes highlighted specific emotional experiences and practical concerns, such as managing a diagnosis, navigating intervention options, and coping with daily life. These findings were organized and discussed in relation to social communication differences and restricted, repetitive behaviors, which are frequently highlighted in discussions about autism. At the same time, we acknowledge the perspective of autistic communities, which view these traits as differences rather than deficits, with many challenges arising from societal expectations and the pressure to mask neurodivergent traits. Together, the results provide a comprehensive overview of the most common topics discussed within autism‐related social media content and offer valuable insights into the lived experiences and motivations for social media engagement among autistic individuals.

Speech is a crucial source of personal information, and the risk of attackers using such information increases day by day. Speaker privacy protection is crucial, and various approaches have been proposed to hide the speaker’s identity. One approach is voice anonymization, which aims to safeguard speaker identity while maintaining speech content through techniques such as voice conversion or spectral feature alteration. The significance of voice anonymization has grown due to the necessity to protect personal information in applications such as voice assistants, authentication, and customer support. Building upon the S3PRL-VC toolkit and on pre-trained speech and speaker representation models, this paper introduces a feature disentanglement approach to improve the de-identification performance of the state-of-the-art anonymization approaches based on voice conversion. The proposed approach achieves state-of-the-art speaker de-identification and causes minimal impact on the intelligibility of the signal after conversion.