Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events.

https://tandfonline.com/doi/pdf/10.1080/13658816.2018.1546390

A conceptual framework for studying collective reactions to events in location-based social media

Social media platforms such as Twitter are extensively used for expressing and exchanging thoughts, opinions, ideas, and feelings, i.e., reactions concerning a topic or an event. Factual information about an event to which people are reacting can be obtained from different types of (geo-)sensors, official authorities, or the public press. However, these sources hardly reveal the emotional or attitudinal impact of events on people, which is, for example, reflected in their reactions on social media. Two approaches that utilize emojis are proposed to obtain the sentiment and emotions contained in social media reactions. Subsequently, these two approaches, along with visualizations that focus on space, time, and topic, are applied to Twitter reactions in the example case of Brexit.

https://www.mdpi.com/2220-9964/8/3/113/pdf

Analyzing and Visualizing Emotional Reactions Expressed by Emojis in Location-Based Social Media

Social media is recognized as the important collaborations in today's information oriented society. Especially, after the global topics like a severe disaster (e.g. the East Japan Great Earthquake, Thailand floods etc.), people all over the world post their thoughts/opinions to social media and their behaviors are sometimes influenced by the discussions on social media. Therefore, we would like to read and understand the contents on the worldwide SNS. Exploring topics on the social media globally is useful to gain a rich insight into the global social contexts. This paper proposes a framework for language independent social media analysis platform to detect the global topics. Our framework targets various countries' social media and extracts keywords from messages written by different languages. Then the framework translates keywords from a local language to English, so that we can understand meanings of keywords. Since our framework is based on keywords and it can extract topics from keywords using data mining techniques, once keywords are extracted, it can be language-independent. In this paper, our proposed framework for language independent social media analysis platform is described with examples of Thai social media analysis results.

Framework for language independent social media analysis platform to detect reactions on global topics

After the East Japan Great Earthquake occurred in Japan on 11th March 2011, many messages related to the earthquake were posted to social media. Especially, in Asian countries, a lot of messages about the earthquake were posted, so that different topics such as concerning about damages by the earthquake, afflicted people were observed, etc. observed. These topics can be recognized as reactions to the earthquake. Therefore, exploring topics related to the earthquake on social media in Asian countries gains a rich insight into the Asian social context. The goal of our research is to analyze Asian people reactions to the East Japan Great Earthquake on social media using data mining technique. As the first step, this paper targets Thai language and conducts the preliminary approximation to investigate topics on Thai social media. We analyze how Thai people reacted to the earthquake and compare with reactions in Japan.

Teeranoot Chauksuvanit

Papers

Comparison of reaction in social media after the East Japan Great Earthquake between Thailand and Japan

Framework for language independent social media analysis platform to detect reactions on global topics