scholarly journals Landscape characterization using photographs from crowdsourced platforms: content analysis of social media photographs

2019 ◽  
Vol 11 (1) ◽  
pp. 558-571 ◽  
Author(s):  
Aitor Àvila Callau ◽  
María Yolanda Pérez Albert ◽  
Joan Jurado Rota ◽  
David Serrano Giné
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Ecosystem Services ◽  
Protected Area ◽  
Depth Information ◽  
Natural Park ◽  
Landscape Characterization ◽  
Automated Content Analysis ◽  
Natural Landscapes

Abstract Landscape characterisation using social media photographs from popular platforms has been proposed as a landscape and ecosystem services approach. However, popular crowdsourced websites provide uncharacterized data and are only representative of the general public. Photographs from crowdsourced sports platforms, whose users are more homogeneous, could help to characterise landscape more uniformly. In this study we use automated content analysis from photographs on Wikiloc, a crowd-sourced sports platform, to characterize landscape in the Ebro Delta Natural Park, a protected area in Spain. Our approach applies big data procedures and spatial analysis to provide in-depth information regarding what draws visitors’ attention to a landscape and to ascertain their intrasite flow. Our results show that sports users are keen on natural landscapes and pay less attention to rural and degraded landscapes, and that areas closer to paths are more photographed than more distant areas.

scholarly journals EXPRESS: “Get a £10 Free Bet Every Week!” – Gambling Advertising on Twitter: Volume, Content, Followers, Engagement and Regulatory Compliance

2021 ◽  
pp. 074391562199967
Author(s):  
Raffaello Rossi ◽  
Agnes Nairn ◽  
Josh Smith ◽  
Christopher Inskip
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Volume Content ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Regulatory Compliance ◽  
The Internet ◽  
Policy Recommendations ◽  
Policy Makers

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

Social media in aid of post disaster management

2020 ◽  
Vol 14 (2) ◽  
pp. 237-260 ◽  
Author(s):  
Ajree Ducol Malawani ◽  
Achmad Nurmandi ◽  
Eko Priyo Purnomo ◽  
Taufiqur Rahman
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Disaster Management ◽  
Practical Implication ◽  
Legal Framework ◽  
Topic Modelling ◽  
Content Type ◽  
The Government ◽  
Post Disaster

Purpose This paper aims to examine tweet posts regarding Typhoon Washi to contend the usefulness of social media and big data as an aid of post-disaster management. Through topic modelling and content analysis, this study examines the priorities of the victims expressed in Twitter and how the priorities changed over a year. Design/methodology/approach Social media, particularly Twitter, was where the data gathered. Using big data technology, the gathered data were processed and analysed according to the objectives of the study. Topic modelling was used in clustering words from different topics. Clustered words were then used for content analysis in determining the needs of the victims. Word frequency count was also used in determining what words were repeatedly used during the course period. To validate the gathered data online, government documents were requested and concerned government agencies were also interviewed. Finding Findings of this study argue that housing and relief goods have been the top priorities of the victims. Victims are seeking relief goods, especially when they are in evacuation centres. Also, the lack of legal basis hinders government officials from integrating social media information unto policymaking. Research limitation This study only reports Twitter posts containing keywords either, Sendong, SendongPH, Washi or TyphoonWashi. The keywords were determined based on the words that trended after Typhoon Washi struck. Practical implication For social media and big data to be adoptable and efficacious, supporting and facilitating conditions are necessary. Structural, technical and financial support, as well as legal framework, should be in place. Maintaining and sustaining positive attitude towards it should be taken care of. Originality/value Although many studies have been conducted on the usefulness of social media in times of disaster, many of these focused on the use of social media as medium that can efficiently spread information, and little has been done on how the government can use both social media and big data in collecting and analysing the needs of the victims. This study fills those gaps in social big data literature.

scholarly journals Social media photo content for Sierra Nevada: a dataset to support the assessment of cultural ecosystem services in protected areas

2020 ◽  
Vol 38 ◽  
pp. 1-12
Author(s):  
Andrea Ros-Candeira ◽  
Ricardo Moreno-Llorca ◽  
Domingo Alcaraz-Segura ◽  
Francisco Javier Bonet-García ◽  
Ana Sofia Vaz
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Ecosystem Services ◽  
Sierra Nevada ◽  
Biosphere Reserve ◽  
Cultural Ecosystem Services ◽  
Earth Observations ◽  
Density Maps ◽  
Different Types ◽  
Types Of Information

This dataset provides crowd-sourced and georeferenced information useful for the assessment of cultural ecosystem services in the Sierra Nevada Biosphere Reserve (southern Spain). Data were collected within the European project ECOPOTENTIAL focused on Earth observations of ecosystem services. The dataset comprises 778 records expressing the results of the content analysis of social media photos published in Flickr. Our dataset is illustrated in this data paper with density maps for different types of information.

scholarly journals Communicating Secure Based Feeling: Content Analysis on Indonesian Police Official Account

2019 ◽  
Vol 8 (6) ◽  
pp. 2541-2543 ◽  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Content Analysis ◽  
Public Relations ◽  
The Past ◽  
Police Official

The development of social media in Indonesia, especially instagram has changed the paradigm of public relations in Indonesia. In the past five years, data from several studies have shown a change in the use of conventional public relations media towards the use of information-based technology and big data. This article will describe descriptively how security is built by Indonesian police social media. This article uses Krippendorf content analysis in collecting data and analyzing data. The results of the study showed that the security was successfully built by the Indonesian police through engagement on Instagram

Assessing cultural ecosystem services of a large marine protected area through social media photographs

2019 ◽  
Vol 176 ◽  
pp. 40-48 ◽  
Author(s):  
Jared Retka ◽  
Paul Jepson ◽  
Richard J. Ladle ◽  
Ana C.M. Malhado ◽  
Felipe A.S. Vieira ◽  
...  
Keyword(s):  
Social Media ◽  
Ecosystem Services ◽  
Protected Area ◽  
Marine Protected Area ◽  
Cultural Ecosystem Services

scholarly journals Using social media images to assess ecosystem services in a remote protected area in the Argentinean Andes

AMBIO ◽  
2019 ◽  
Vol 49 (6) ◽  
pp. 1146-1160 ◽  
Author(s):  
Sebastian Dario Rossi ◽  
Agustina Barros ◽  
Chelsey Walden-Schreiner ◽  
Catherine Pickering
Keyword(s):  
Social Media ◽  
Ecosystem Services ◽  
Protected Area ◽  
Media Images

Evaluating universities' strategic online communication: how do Shanghai Ranking's top 50 universities grow stakeholder engagement with Facebook posts?

2020 ◽  
Vol 24 (3) ◽  
pp. 265-283 ◽  
Author(s):  
Birte Fähnrich ◽  
Jens Vogelgesang ◽  
Michael Scharkow
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Online Communication ◽  
Content Type ◽  
Global Brands ◽  
Automated Content Analysis ◽  
Shanghai Ranking ◽  
Fan Engagement ◽  
Media Channel

PurposeThis study is dedicated to universities' strategic social media communication and focuses on the fan engagement triggered by Facebook postings. The study contributes to a growing body of knowledge that addresses the strategic communication of universities that have thus far hardly dealt with questions of resonance and evaluation of their social media messages.Design/methodology/approachUsing the Facebook Graph API, the authors collected posts from the official Facebook fan pages of the universities listed on Shanghai Ranking's Top 50 of 2015. Specifically, the authors retrieved all posts in a three-year range from October 2012 to September 2015. After downloading the Facebook posts, the authors used tools for automated content analysis to investigate the features of the post messages.FindingsOverall, the median number of likes per 10,000 fans was 4.6, while the number of comments (MD = 0.12) and shares (MD = 0.40) were considerably lower. The average Facebook Like Ratio of universities per 10,000 fans was 17.93%, the average Comment Ratio (CR) was 0.56% and the average Share Ratio (SR) was 2.82%. If we compare the average Like Ratios (17.93%) and Share Ratios (2.82%) of the universities with the respective Like Ratios (5.90%) and Share Ratios (0.45%) of global brands per 10,000 fans, we may find that universities are three times (likes) and six times (shares) as successful as are global brands in triggering engagement among their fan bases.Research limitations/implicationsThe content analysis was solely based on the publicly observable Facebook communication of the Top 50 Shanghai Ranking universities. Furthermore, the content analysis was limited to universities listed on the Shanghai Ranking's Top 50. Also, the Facebook posts have been sampled between 2012 and September 2015. Moreover, the authors solely focused on one social media channel (i.e., Facebook), which might restrict the generalizability of the study findings. The limitations notwithstanding, university communicators are invited to take advantage of the study's insights to become more successful in generating fan engagement.Practical implicationsFirst, posts published on the weekend generate significantly more engagement than those published on workdays. Second, the findings suggest that posts published in the evening generate more engagement than those published during other times of day. Third, research-related posts trigger a certain number of shares, but at the same time these posts tend to lower engagement with regard to liking and commenting.Originality/valueTo the authors’ best knowledge, the automated content analysis of 72,044 Facebook posts of universities listed in the Top 50 of the Shanghai Ranking is the first large scale longitudinal investigation of a social media channel of higher education institutions.

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