Spectre of you: Social data Australians cannot control and the data broker industry built on it

2021 ◽  
Vol 17 (2) ◽  
pp. 191-198 ◽  
Author(s):  
Belinda Barnet
Keyword(s):  
Social Data ◽  
Data Broker

Analyzing Social Data: A Statistical Orientation.Karl Schuessler

1973 ◽  
Vol 79 (2) ◽  
pp. 495-497
Author(s):  
James Fennessey
Keyword(s):  
Social Data

The Generation and Utilization of Social Data

Ethics ◽  
1973 ◽  
Vol 84 (1) ◽  
pp. 22-37
Author(s):  
Norman M. Bradburn
Keyword(s):  
Social Data

scholarly journals Correction to: An ethico-legal framework for social data science

2021 ◽  
Author(s):  
Nikolaus Forgó ◽  
Stefanie Hänold ◽  
Jeroen van den Hoven ◽  
Tina Krügel ◽  
Iryna Lishchuk ◽  
...  
Keyword(s):  
Data Science ◽  
Legal Framework ◽  
Social Data

scholarly journals Mapping Urban Land Use by Using Landsat Images and Open Social Data

2016 ◽  
Vol 8 (2) ◽  
pp. 151 ◽  
Author(s):  
Tengyun Hu ◽  
Jun Yang ◽  
Xuecao Li ◽  
Peng Gong
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Landsat Images ◽  
Social Data

What are students thinking and feeling Understanding them from social data mining

2021 ◽  
Vol 65 (2) ◽  
pp. 110
Author(s):  
Hua Zhao ◽  
Yang Zuo ◽  
Chunming Xu ◽  
Hengzhong Li
Keyword(s):  
Data Mining ◽  
Social Data ◽  
Social Data Mining

Multi-task Learning for Spatial Events Prediction from Social Data

2021 ◽  
Author(s):  
Sungkwang Eom ◽  
Byungkook Oh ◽  
Sangjin Shin ◽  
Kyong-Ho Lee
Keyword(s):  
Social Data ◽  
Task Learning

Extracting insights from big social data for smarter tourism destination management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gianluca Solazzo ◽  
Ylenia Maruccia ◽  
Gianluca Lorenzo ◽  
Valentina Ndou ◽  
Pasquale Del Vecchio ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Destination Image ◽  
Data Types ◽  
Social Data ◽  
Content Type ◽  
Management And Development ◽  
Destination Attractiveness ◽  
Tourist Demand ◽  
Tourist Behaviour

Purpose This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image. Design/methodology/approach This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study. Findings Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination. Originality/value The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

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