Ein wichtiges Anliegen der Abteilung "Computational Social Science" ist es, die interdisziplinäre Community für diesen Bereich in Deutschland und Europa zu stärken und die weltweite Vernetzung voranzutreiben. Ein wichtiges Mittel hierfür sind internationale Konferenzen, Symposien und Workshops.
Unter den von GESIS in den letzten Jahren organisierten Veranstaltungen waren die 3rd International Conference on Computational Social Science (IC2S2 2017) und die 10th International AAAI Conference on Web and Social Media (ICWSM-16), die European Symposium Series on Societal Challenges in Computational Social Science, der KickOff Workshop International Research in Computational Social Science und viele andere.
GESIS hat eine Google Group eingerichtet, die allen offensteht, die sich für Computational Social Science interessieren. Die Gruppe soll als CSS-Forum genutzt werden, um Ideen auszutauschen, offene Stellen zu annoncieren, Veranstaltungen bekanntzumachen und generell, um Kontakte in der Community zu erleichtern und zu festigen. Forscherinnen und Forscher aller Disziplinen sind herzlich eingeladen, der Gruppe beizutreten: http://tiny.cc/cssnet
Data Science is the interdisciplinary science of the extraction of interpretable and useful knowledge from digital datasets. Due to the rapid surge of digital trace data (often as “Big Data”) in a wide range of application areas, Data Science is also increasingly utilized in the social sciences and humanities. In contrast to empirical social science, Data Science methods often serve purposes of exploration and inductive inference. In this course, we aim to provide an introductory overview on the field of Data Science for practitioners. In particular, we want to impart basic understanding of the main methods and algorithms and understand how these can be deployed in practical application scenarios, focusing on the analysis of large behavioral data found on the Web. For that purpose, our schedule alternates between lecture sessions that present the theoretical and technical background of data analysis and practical sessions that allow participants to directly apply acquired knowledge with simple code in the Python programming language. We cover aspects of data collection, preprocessing, interactive exploration, regression analysis, hypothesis testing, machine learning, and network analysis using basic Python and key packages.
Participants will obtain profound knowledge about typical data types and structures encountered when dealing with behavioral traces from the Web, state-of-the art data analysis methods, and they will learn how this approach differs from those typically encountered in survey-based or experimental research. This will enable them to identify benefits and pitfalls of these methods in their field of interest and will, thus, allow them to select and appropriately apply data analysis and machine-learning methods for large datasets in their own research. The knowledge obtained in this course provides a starting point that enables participants to investigate specialized methods for their individual research projects.
Previous knowledge on (i) basic inference statistics (e.g., linear Regression, T-Tests), and (ii) a programming or at least scripting language (e.g., R, Syntax-Code in SPSS, Stata) is very advantageous to follow the coursework. In any case, to ensure a common starting level between participants attendants will be asked to familiarize themselves with the most basic concepts of Python such as variables, lists, and loops via material that will be provided to all participants through the e-learning platform ILIAS beforehand. This material will be recapitulated briefly in the beginning of the course.
Please note that participants have to bring their own laptop for this course. All utilized software is available without cost as open source under Windows, MacOS, and Linux systems. Detailed instructions for installing the needed software and doing the introductory exercises will be provided before the start of the course.
Excellent training in research methods is crucial for any field. Especially emerging fields like Computational Social Science which are not yet institutionalized in universities need opportunities to teach their methods to ensure excellent future research.
Each of three CSS summer schools will focus on a specific type of data (behavioral trace data, text data or multimedia data), their corresponding methods and selected relevant topics in social science.
The first summer school (2017) was on methods for analyzing and modeling behavioral trace data (e.g., sequential learning methods, models of user navigation and click streams, interaction network analysis, massive online experiments that allow to observe interactions) and students conducted projects in which they applied the newly learned methods to gain insights into social phenomena like prosocial behavior, consensus or mobility.
The second summer school (2018) will focus on methods for textual data (e.g., sentiment analysis, latent semantic models, causal inference methods for text data). The topical focus of student's project work will be conflicts, radicalization, polarization and bias, since online discussions, blog posts and reactions to news articles may allow to understand why and how people radicalize over time.
The third summer school (2019) will go beyond text and focus on methods for analyzing multimedia data (e.g., computer vision methods, spatial and temporal analysis of urban spaces via multimedia content analysis, spreading and mutation models for multimedia content). In this school we will encourage and help students to explore cultural phenomena like social orientation and its expression for instance in urban spaces, images and art.
The event series is funded by Volkswagen Foundation.
Please find further information on the CSS Summer School Website: summerschool.computationalsocialscience.eu
The Symposium is an interdisciplinary venue that brings together researchers from a diverse range of disciplines to contribute to the definition and exploration of the societal challenges in Compuational Social Science, especially around the topics of inequality and imbalance to understand the role that digital technologies, the Web, and the algorithms used therein play in the mediation and creation of inequalities, discrimination and polarization. The series is funded by Volkswagen Foundation.
The second event of the symposium series will take place in Cologne, Germany. The overall subject will be bias and discrimination. We are happy to keep you individually informed about calls and program, please register your e-mail-address here or consider subscribing to the CSSNET Google group we established. More information can be found on the symposium website.
Das GESIS Computational Social Science (CSS) Seminar ist Teil der öffentlichen Vorträge der GESIS-Vortragsreihe, an der alle Interessierten vor Ort teilnehmen können (GESIS am Standort Köln, Unter Sachsenhausen 6-8, 50667 Köln). Das CSS Seminar ist eine englischsprachige, etwa monatlich stattfindende Veranstaltung zum Expertenaustausch rund um die Themen Data Science und Social Analytics. Genauere Informationen zu den Vorträgen finden Sie hier.