Mitigating the Impact of Data Sampling on Social Media Analysis and Mining

2020 ◽  
Vol 7 (2) ◽  
pp. 546-555
Author(s):  
Kuai Xu ◽  
Feng Wang ◽  
Haiyan Wang ◽  
Yufang Wang ◽  
Ying Zhang
2021 ◽  
Vol 17 (4) ◽  
pp. 89-108
Author(s):  
Chutisant Kerdvibulvech ◽  
Pattaragun Wanishwattana

Computational journalism, especially social media analysis, is a very popular field in computational science. This study was conducted to explore and analyze the impact of the intensity of the exposure to social media on young Thai adults' body images and attitudes toward plastic surgery. The purposive sampling method was used for choosing 250 young Thai men and women aged 21 to 40 who used Facebook and/or Instagram on a regular basis. Online survey questionnaires were posted on Facebook for one month to achieve the results. It was found that young Thai adults frequently and heavily used both social media. Having appearance pressure from and repeated social comparison with idealistic media images, a considerable number of participants displayed more negative self-perceptions and engaged in appearance-changing strategies through increased appearance investment. The results showed that the more these young adults were exposed to social media, the more they were likely to develop a negative body image of themselves, which later caused their attitude toward plastic surgery to be positive.


2022 ◽  
pp. 488-509
Author(s):  
Ciro Clemente De Falco ◽  
Noemi Crescentini ◽  
Marco Ferracci

In the data revolution era, the availability of “voluntary” and “derived from social media” geographic information allowed the spatial dimension to gain attention in digital and web studies. The purpose of this work is to recognize the impact of this research stream on some methodological and theoretical issues. The first regards “critical algorithm studies” in order to understand what algorithms are used. The second concerns how these works conceive the space. The last two issues concern the disciplinary areas in which these researches take place and which are the ecological units taken into account. The authors answer these questions by analyzing, through a content analysis, the researches extracted with the PRISMA methodology that have used Twitter as a data source. The application of this procedure allows the authors to classify the analysis material, moving simultaneously on the four defined dimensions.


2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 694-694
Author(s):  
Tammy Mermelstein

Abstract Preparing for or experiencing a disaster is never easy, but how leaders communicate with older adults can ease a situation or make it exponentially worse. This case study describes two disasters in the same city: Hurricane Harvey and the 2018 Houston Texas Ice Storm and the variation in messaging provided to and regarding older adults. For example, during Hurricane Harvey, the primary pre-disaster message was self-preparedness. During the storm, messages were also about individual survival. Statements such as “do not [climb into your attic] unless you have an ax or means to break through,” generated additional fear for older adults and loved ones. Yet, when an ice storm paralyzed Houston a few months later, public messaging had a strong “check on your elderly neighbors” component. This talk will explore how messaging for these events impacted older adults through traditional and social media analysis, and describe how social media platforms assisted people with rescue and recovery. Part of a symposium sponsored by Disasters and Older Adults Interest Group.


2021 ◽  
Author(s):  
Tasnim M. A. Zayet ◽  
Maizatul Akmar Ismail ◽  
Kasturi Dewi Varathan ◽  
Rafidah M. D. Noor ◽  
Hui Na Chua ◽  
...  

Author(s):  
Ariel A. Williamson ◽  
Jodi Mindell ◽  
Olivia Cicalese ◽  
Abigail Varker ◽  
Mikayla Carson

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