Mar 02 2020 10:00 - Mar 06 2020 19:00

【CANCELLED】2020 UTokyo LAINAC International Conference “Global Event Analysis: Big Data, Natural Language Processing, Early Warning, and Data Visualization”


Overview of the Conference
Social scientists have used various forms of “event data”—a catalogue of political events such as civil violence, riots, international conflicts, and popular protests—to understand chronological changes, geospatial variations, and dynamics of political phenomena of interest. With advent of new methodological innovations including natural language processing (NLP) and big data approaches, an increasing number of scholars have started to integrate these innovations into their event data analysis. One of the main objectives of this conference is to share experiences of the scholars who are specialized in one or more of the following research areas and techniques: event analysis, NLP, big data analysis, computer sciences, computational linguistics, forecasting and early warnings, data visualization, and other relevant fields. We will explore potentials and limitations of event analysis based on the innovative methods. We welcome research papers and courses addressing the following topics:
To construct a database of political, economic, and social events, scholars have used mass media such as newspapers as data source and coded manually to extract information about political events from these sources. However, a variety of techniques associated with NLP and big data approaches may revolutionize the ways of event data development.
  • Do the NLP techniques enable new ways of building event data?
  • What are the advantages and disadvantages of the event databases constructed by using some of the NLP techniques in comparison with the ones built manually?
  • What is the prospect of using the SNS such as Twitter as a source of event data? What are the potentials and problems associated with the use of the SNS in event data analysis?
  • Event data that are useful for social science research typically include the information about (1) who (2) did what (3) to whom, (4) when, (5) where, and (6) why. In other words, scholars would like to extract information about (1) Actor, (2) Action, (3) Target, (4) Time, (5) Location, and (6) Claim/Goal of event. Which of the six types of information can the new methods based on NLP and big data approaches extract reliably and which not? What are the reasons of the difficulty of extracting the information in a consistent and reliable manner?
  • If event data projects succeed in forecasting the occurrence of political violence around the world in a timely manner, the success will improve significantly the ability of policymakers and academics to prevent such disasters in the future. To make timely predictions of political disasters such as violence and civil war, it is vital to convert a massive number of news reports instantaneously on a real time basis into a data set for subsequent analyses. What kind of event data system is more desirable for such a purpose?
  • What types of analysis can be done using event data that are developed using the NLP and big data approaches?
  • What kinds of research questions can be addressed by NLP-based event data, and what kind of questions that can be addressed by manually-build event data cannot be addressed by NLP-based event data?
  • How can we explore geographical information in event data and apply GIS (geographic information systems) methods and visualize the findings?
  • How can we explore relational information in event data and apply network analytic methods to the data?
  • How can we predict civil violence using event data? The outbreak of violent conflicts often catches us by surprise. Why do people who have lived peacefully side-by-side for years suddenly start to behave violently toward each other? Why does political violence tend to concentrate in large waves, leading to tragic outcomes ranging from riots, to ethnic cleansing, genocide, and civil wars? How to develop analytic models predicting political violence theoretically and methodologically?
Our goal is to compile proceedings from the conference as a LAINAC report booklet. Each contributor is expected to develop his/her contribution for publication based on the feedback he/she will get during the sessions.

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