A market map provides managers with a static snapshot of the competitive positions of a market’s participants (such as products or brands). Today, most markets are rather large (e.g., comprising hundreds of products so that a comprehensive visualization of competitive market structures can be cumbersome and complex. Yet, reduction of the analysis to smaller representative product sets can obscure important information. The first part of the workshop outlines (i) data sources to derive consideration sets of consumers that reflect competition between products and (ii) approaches (e.g., building upon social network analysis) that integrate these data into a modeling and mapping approach to visualize competition in large markets and to identify distinct submarkets.

Yet, as markets tend to be in flux, knowledge about the trajectories of competitive positions of market’s participant over time would be more informative than a static snapshot. In contrast to static snapshots, trajectories create a forward-looking perspective on competition, reveal whether positions are converging or diverging and help managers evaluate the impact of their positioning efforts. Although data for market structure analysis is increasingly available in high frequency (see part 1 of the workshop), extant mapping methods are exclusively static, and do not reveal market participants’ trajectories. Therefore, I focus in part 2 of the workshop on dynamic mapping method that generate a sequence of cohesive maps that enable market analysts to track the trajectories of competitive positions over time.

The increasing availability of digital behavioral trace (DBT) data promises novel social science studies that simultaneously scale up on a large number of study participants and zoom in on fine-grained individual behavioral actions. These data may, for example, stem from social media platforms, social sensor experiments, or wearable technologies such as smart phones or watches. DBT data offer a seemingly objective perspective on how people behave individually and socially – how they eat, sleep, travel, interact, socialise, and date. DBT network data often come in the form of relational events – time-stamped dyadic observations that can be represented as time-ordered edge lists. Several new models for the statistical analysis of relational events have been proposed over the past years. The first part of the course will cover the statistical analysis of DBT data with relational event models.

Studies that merely rely on DBT data have some obvious blind spots. Individual behavior is to a large extent based on how individuals perceive their environment, their relationships, and themselves. Such perception data can be well collected through traditional surveys. Survey data also have known challenges such as cognitive burdens, necessary time investments by participants, and measurement biases. Traditional social network data often stem from surveys and may represent who individuals perceive as friends, whom they like or dislike, and whom they trust. Dynamic network data collected through surveys can, for example, be statistically analysed with stochastic actor-oriented models. These models will be briefly discussed in the second part of the course.

Social media metrics (altmetrics) refers to metrics derived from social media platforms (such as Facebook, Twitter, Wikipedia, mainstream news websites, etc.). These metrics offer possibility of studying the relations and interactions between social media users, scholarly contents, and different actors. Altmetrics data aggregators provide access to social media metrics differ in terms of methodological choices in collecting, updating, tracking, and reporting metrics. This course focuses on defining and interpreting social media metrics, data possibilities and challenges, social media metrics data analysis and their uses and applications.

The topic of “preferences” has attracted increasing attention in artificial intelligence and machine learning in the recent past, where it has emerged as an interdisciplinary research field with close connections to operations research, social choice, and the decision sciences. Roughly speaking, preference learning is about methods for learning preference models from explicit or implicit information about the preferences of an individual or a group of individuals, and the use of such models for predicting preferences in new situations. Approaches relevant to this field range from learning special types of preference models, such ranking functions, to collaborative filtering techniques for recommender systems. The goal of this talk is to provide a brief introduction to the field of preference learning and, moreover, to elaborate on the use of preference learning in the context of social media, notably for learning user preferences from social media data.