Microblogging platforms such as Twitter, receive massive messages during crisis events. Real-time insights are crucial for emergency response. Hence, there is a need to develop faithful tools for efficiently digesting information. In this paper, we present CrisICSum, a platform for classification and summarization of crisis events. The objective of CrisICSum is to classify user posts during disaster events into different humanitarian classes (i.e., damage, affected people, etc.) and generate summaries of class-level messages. Unlike existing systems, CrisICSum employs an interpretable by design backend classifier. It can generate explanations for output decisions. Besides, the platform allows user feedback on both classification and summarization phases. CrisICSum is designed and run as an easily integrated web application. Backend models are interchangeable. The system can assist users and human organizations in improving response efforts during disaster situations. CrisICSum is available at https://crisicsum.l3s.uni-hannover.de

Our perception of the situation in a country or a region is strongly influenced by the reflection of this situation in mass and social media channels. This effect is even more pronounced for geographically and culturally distant regions, for which no firsthand experience is available. To avoid information overload, news outlets typically filter the available news from foreign countries based on the expected interest of the target audiences. Such filtering imposes an inherent bias in the reporting and can create a distorted perception of a region among the consumers of news of other regions. This might lead to misunderstandings between countries and unsubstantiated political and individual decisions (e.g., in the context of migration). In this article, we systematically analyze the bias created in news reports. We consider Europe, or more precisely the European Union (EU) as ourzone of concern, and examine its image in the media (news outlets) of other regions, Europe(NON-EU), Africa, Asia, Middle-East, America, and Oceania. An analysis of the year 2018 (January–December 2018) of news published in those regions reveals marked differences in the editorial policies and presented narrative when dealing with EU-related news. We observe a significant variation in the sentiment polarity of the reported EU-related stories between the European and other regional news outlets. We further analyze the polarity variation among different subregions of large geographical areas, such as Africa, Asia, and America. We observe a contrasting difference in their editorial policies. This trend also holds for news related to different topics, such as politics, business, economy, health, and international relation.

Social graph construction from various sources has been of interest to researchers due to its application potential and the broad range of technical challenges involved. The World Wide Web provides a huge amount of continuously updated data and information on a wide range of topics created by a variety of content providers, and makes the study of extracted people networks and their temporal evolution valuable for social as well as computer scientists. In this paper we present SocGraph - an extraction and exploration system for social relations from the content of around 2 billion web pages collected by the Internet Archive over the 17 years time period between 1996 and 2013. We describe methods for constructing large social graphs from extracted relations and introduce an interface to study their temporal evolution.