scholarly journals How Social Media Text Analysis Can Inform Disaster Management

2018 ◽  
pp. 199-207 ◽  
Author(s):  
Sabine Gründer-Fahrer ◽  
Antje Schlaf ◽  
Sebastian Wustmann
Keyword(s):  
Social Media ◽  
Disaster Management ◽  
Text Analysis ◽  
Social Media Text

Urban Region Function Mining Service Based on Social Media Text Analysis

2021 ◽  
Vol 31 (04) ◽  
pp. 563-586
Author(s):  
Yanchun Sun ◽  
Hang Yin ◽  
Jiu Wen ◽  
Zhiyu Sun
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Human Activities ◽  
Web Application ◽  
Classification Model ◽  
Urban Region ◽  
Urban Regions ◽  
Social Media Text ◽  
Urban Function ◽  
High Level

Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions’ functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region’s main functions, we design a service which extracts human activities using Sina Weibo ( www.weibo.com ; the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region’s main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions’ function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method.

A deep learning-based social media text analysis framework for disaster resource management

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ashutosh Bhoi ◽  
Sthita Pragyan Pujari ◽  
Rakesh Chandra Balabantaray
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Resource Management ◽  
Text Analysis ◽  
Analysis Framework ◽  
Social Media Text

scholarly journals What Articles On Measles Europeans Share On Social Media: Text Analysis Study (Preprint)

10.2196/30150 ◽  
2021 ◽  
Author(s):  
Dominik Wawrzuta ◽  
Mariusz Jaworski ◽  
Joanna Gotlib ◽  
Mariusz Panczyk
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Analysis Study ◽  
Social Media Text

scholarly journals The New Normal in Indonesia: A Twitter Based Social Media Analysis

2021 ◽  
Vol 41 ◽  
pp. 04004
Author(s):  
Dhite Bayu Nugroho ◽  
Mawaddah Ar Rochmah ◽  
Faridatun Khasanah ◽  
Fajar Fatmawati ◽  
Al Razi Sena
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Text Analysis ◽  
Physical Distance ◽  
Media Analysis ◽  
Social Media Analysis ◽  
New Normal ◽  
Time Period ◽  
Normal Period ◽  
Social Media Text

The Indonesian government began using the term “new normal” in mid-May 2020, which prompted debate in the community and was reflected on social media. Therefore, the goal of this study was to use Twitter-based social media text analysis to depict the Indonesian public’s impression of new normal conditions during the COVID-19 epidemic. We performed a text analysis on Twitter using the phrases “new normal” and “kenormalaan baru” with a time period of 1-31 July 2020 and location restrictions in Indonesia. The words associated with “new normal” are then described in a word cloud map and sorted in a flipped bar chart. We also performed a network bi-gram network analysis to identify word correlations in order to identify sentiments from Twitter text. When compared to other words, the word “covid” has the highest frequency. Other words linked with health protocols, such as “cuci” (wash), “tangan” (hand), “jaga” (maintain), and “jarak” (distance), appeared 1,138, 1501, 3.343, and 2.131 times, respectively, according to unigram analysis. Bigram network analysis reveals discrete clusters of phrases such as “protokol kesehatan” (health protocol), “wash hands” (cuci tangan), “jaga jarak” (physical distance), and “wear mask” (pakai masker). The word connections “covid,” “pandemi” (pandemic), “lupa” (forget), “maskernya” (the mask), “lakukan” (do), “social”, “distancing”, “luar” (outside), “rumah” (home) also conveyed a remark about standard measures in the new normal period.

Modeling Factuality Judgments in Social Media Text

2014 ◽  
Author(s):  
Sandeep Soni ◽  
Tanushree Mitra ◽  
Eric Gilbert ◽  
Jacob Eisenstein
Keyword(s):  
Social Media ◽  
Social Media Text

Bi-LSTM and Ensemble based Bilingual Sentiment Analysis for a Code-mixed Hindi-English Social Media Text

2020 ◽  
Author(s):  
Konark Yadav ◽  
Aashish Lamba ◽  
Dhruv Gupta ◽  
Ansh Gupta ◽  
Purnendu Karmakar ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Text
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