scholarly journals The Geographies of Expatriates’ Cultural Venues in Globalizing Shanghai: A Geo-Information Approach Applied to Social Media Data Platform

2021 ◽  
Vol 10 (8) ◽  
pp. 524
Author(s):  
Xiang Feng ◽  
Peipei Wu ◽  
Wei Shen ◽  
Qian Huang
Keyword(s):  
Social Media ◽  
Cultural Consumption ◽  
Cultural Patterns ◽  
Social Media Data ◽  
Information Approach ◽  
Starting Point ◽  
Data Platform ◽  
Space Economy ◽  
Media Data ◽  
Geographical Characteristics

This paper measures the cultural consumption patterns of expatriates in Shanghai by applying a geo-information approach to data derived from social media. In order to reveal the geographical characteristics, the paper zooms in on the level of city districts and presents a typology based on the degree of spatial and functional aggregation of cultural venues. Three major contextual parameters underlying the typology are discerned: the geographies of the Shanghai space-economy, the imprint of Shanghai’s spatio-political strategies, and the overall policy approach toward this community. We discuss how this study can be used as the starting point for further comparative studies on cultural patterns of expatriates in other geographical contexts.

scholarly journals A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

2020 ◽  
Vol 17 (13) ◽  
pp. 4752 ◽  
Author(s):  
Rodrigo Martínez-Castaño ◽  
Juan C. Pichel ◽  
David E. Losada 
Keyword(s):  
Social Media ◽  
Real Time ◽  
Public Health Surveillance ◽  
Time Analysis ◽  
Social Media Data ◽  
Real Time Processing ◽  
Processing Elements ◽  
Real Time Analysis ◽  
Data Platform ◽  
Media Data

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.

scholarly journals A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets for Transportation Research

2020 ◽  
Author(s):  
Mahmoud Arafat
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Work Related ◽  
Large Scale Data ◽  
Software Models ◽  
Starting Point ◽  
Transportation Research ◽  
Media Data

<p>In response to the Coronavirus disease (COVID-19) outbreak and the Transportation Research Board’s (TRB) urgent need for work related to transportation and pandemics, this paper contributes with a sense of urgency and provides a starting point for research on the topic. The main goal of this paper is to support transportation researchers and the TRB community during this COVID-19 pandemic by reviewing the performance of software models used for extracting large-scale data from Twitter streams related to COVID-19. The study extends the previous research efforts in social media data mining by providing a review of contemporary tools, including their computing maturity and their potential usefulness. The paper also includes an open repository for the processed data frames to facilitate the quick development of new transportation research studies. The output of this work is recommended to be used by the TRB community when deciding to further investigate topics related to COVID-19 and social media data mining tools.</p>

scholarly journals A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets for Transportation Research

2020 ◽  
Author(s):  
Mahmoud Arafat
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Large Scale ◽  
Social Media Data ◽  
Work Related ◽  
Large Scale Data ◽  
Software Models ◽  
Starting Point ◽  
Transportation Research ◽  
Media Data

<p>In response to the Coronavirus disease (COVID-19) outbreak and the Transportation Research Board’s (TRB) urgent need for work related to transportation and pandemics, this paper contributes with a sense of urgency and provides a starting point for research on the topic. The main goal of this paper is to support transportation researchers and the TRB community during this COVID-19 pandemic by reviewing the performance of software models used for extracting large-scale data from Twitter streams related to COVID-19. The study extends the previous research efforts in social media data mining by providing a review of contemporary tools, including their computing maturity and their potential usefulness. The paper also includes an open repository for the processed data frames to facilitate the quick development of new transportation research studies. The output of this work is recommended to be used by the TRB community when deciding to further investigate topics related to COVID-19 and social media data mining tools.</p>

scholarly journals Classifying patient and professional voice in social media health posts

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Beatrice Alex ◽  
Donald Whyte ◽  
Daniel Duma ◽  
Roma English Owen ◽  
Elizabeth A. L. Fairley
Keyword(s):  
Social Media ◽  
Skin Diseases ◽  
Document Classification ◽  
Training Data ◽  
Social Media Data ◽  
Patient Voice ◽  
Starting Point ◽  
Data Source ◽  
Professional Voice ◽  
Media Data

Abstract Background Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases). Results We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only. Conclusion The main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data
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