The concept of category has been the subject of multiple works in various disciplines. In this paper, we attempt to provide a perspective on this concept with regard to the issues of equity, diversity, and inclusion in the context of data science. More precisely, this concerns discussing non-neutrality in the analysis according to the categories and their modalities, questioning who builds them and how, and addressing the issues of multidisciplinary contributions and the posture as a data scientist. This paper is also an opportunity to discuss the articulation of academics and activism issues around these categories. This position paper aims to reflect on the issues of categorization.

This paper provides a multi-perspective vision of diversity and inclusion (D&I) projects aiming to promote equity in organisations seeking to build virtuous contexts where people can achieve positive professional and personal objectives. It introduces the understanding of D&I, best practices and outcomes of projects promoted in multicultural organisations, including academia, universities and research centres (Politecnico di Torino, university education in France and the French CNRS) and in leading international companies, namely Accenture and Nestlé. The paper gathers and extends the discussion and ideas exchanged in the D&I panel of the conference ADBIS-2022.

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (Mention-Anomaly-Based Event Detection), a novel method that leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect important events and estimate the magnitude of their impact over the crowd. The main advantages of MABED over prior works are that (i) it relies solely on tweets, meaning no external knowledge is required, and that (ii) it dynamically estimates the period of time during which each event is discussed rather than assuming a predefined fixed duration. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Last, we show that MABED helps with the interpretation of detected events by providing clear and precise descriptions.

This paper describes SONDY, a tool for analysis of trends and dynamics in online social network data. SONDY addresses two audiences: (i) end-users who want to explore social activity and (ii) researchers who want to experiment and compare mining techniques on social data. SONDY helps end-users like media analysts or journalists understand social network users interests and activity by providing emerging topics and events detection as well as network analysis functionalities. To this end, the application proposes visualizations such as interactive time-lines that summarize information and colored user graphs that reflect the structure of the network. SONDY also provides researchers an easy way to compare and evaluate recent techniques to mine social data, implement new algorithms and extend the application without being concerned with how to make it accessible. In the demo, participants will be invited to explore information from several datasets of various sizes and origins (such as a dataset consisting of 7,874,772 messages published by 1,697,759 Twitter users during a period of 7 days) and apply the different functionalities of the platform in real-time.

Hierarchies are crucial for analysis in data warehouses. But they can hardly be defined on measure attributes. In this paper, we tackle this issue and we show that measure generalizations often depend on a context. For instance, a given blood pressure can be either low, normal or high regarding not only the collected measure but also characteristics of the patient such as the age. The contribution of this paper is threefold. (1) Thanks to an external database storing the expert knowledge, we propose an effective solution for considering these hierarchies. (2) In order to efficiently manage this knowledge, a Rich Internet Application is developed. (3) Finally, in order to provide a flexible analysis, query rewriting module is proposed. Thus, it is possible to answer queries such as: "Who had a low blood pressure last night?''