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 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 Deep learning assessment of cultural ecosystem services from social media images

2021 ◽  
Author(s):  
Ana Sofia Cardoso ◽  
Francesco Renna ◽  
Domingo Alcaraz-Segura ◽  
Ana Sofia Vaz
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Ecosystem Services ◽  
Sierra Nevada ◽  
Binary Classification ◽  
Model Performance ◽  
Learning Assessment ◽  
Cultural Ecosystem Services ◽  
Great Relevance

Crowdsourced social media data has become popular in the assessment of cultural ecosystem services (CES). Advances in deep learning show great potential for the timely assessment of CES at large scales. Here, we describe a procedure for automating the assessment of image elements pertaining to CES from social media. We focus on a binary (natural, human) and a multiclass (posing, species, nature, landscape, human activities, human structures) classification of those elements using two Convolutional Neural Networks (CNNs; VGG16 and ResNet152) with the weights from two large datasets - Places365 and ImageNet -, and our own dataset. We train those CNNs over Flickr and Wikiloc images from the Peneda-Geres region (Portugal) and evaluate their transferability to wider areas, using Sierra Nevada (Spain) as test. CNNs trained for Peneda-Geres performed well, with results for the binary classification (F1-score > 80%) exceeding those for the multiclass classification (> 60%). CNNs pre-trained with Places365 and ImageNet data performed significantly better than with our data. Model performance decreased when transferred to Sierra Nevada, but their performances were satisfactory (> 60%). The combination of manual annotations, freely available CNNs and pre-trained local datasets thereby show great relevance to support automated CES assessments from social media.

Enriching social media data allows a more robust representation of cultural ecosystem services

2021 ◽  
Vol 50 ◽  
pp. 101328
Author(s):  
Nathan Fox ◽  
Laura J. Graham ◽  
Felix Eigenbrod ◽  
James M. Bullock ◽  
Katherine E. Parks
Keyword(s):  
Social Media ◽  
Ecosystem Services ◽  
Cultural Ecosystem Services ◽  
Social Media Data ◽  
Robust Representation ◽  
Media Data

Tweeting about TV: Sharing television viewing experiences via social media message streams

First Monday ◽  
2011 ◽  
Author(s):  
D. Yvette Wohn ◽  
Eun-Kyung Na
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Television Viewing ◽  
Different Types ◽  
Program Content ◽  
Media Message

Through content analysis of messages posted on Twitter, we categorize the types of content into a matrix — attention, emotion, information, and opinion. We use this matrix to analyze televised political and entertainment programs, finding that different types of messages are salient for different types of programs, and that the frequencies of the types correspond with program content. Our analyses suggest that Twitter picks up where formal social television systems failed: people are using the tool to selectively seek others who have similar interests and communicate their thoughts synchronous with television viewing.

scholarly journals You're hired: examining acceptance of social media screening of job applicants

2021 ◽  
Author(s):  
Anatoliy Gruzd ◽  
Jenna Jacobson ◽  
Elizabeth Dubois
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Information Privacy ◽  
Job Applicants ◽  
Comfort Level ◽  
Hiring Practices ◽  
Related Information ◽  
Different Types ◽  
Prospective Employer ◽  
Types Of Information

The paper examines attitudes towards employers using social media to screen job applicants. In an online survey of 454 participants, we compare the comfort level with this practice in relation to different types of information that can be gathered from publicly accessible social media. The results revealed a nuanced nature of people’s information privacy expectations in the context of hiring practices. People’s perceptions of employers using social media to screen job applicants depends on (1) whether or not they are currently seeking employment (or plan to), (2) the type of information that is being accessed by a prospective employer (if there are on the job market), and (3) their cultural background, but not gender. The findings emphasize the need for employers and recruiters who are relying on social media to screen job applicants to be aware of the types of information that may be perceived to be more sensitive by applicants, such as social network-related information. Keywords : social media, information privacy, job screening, hiring practices

scholarly journals You're hired: examining acceptance of social media screening of job applicants

2021 ◽  
Author(s):  
Anatoliy Gruzd ◽  
Jenna Jacobson ◽  
Elizabeth Dubois
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Information Privacy ◽  
Job Applicants ◽  
Comfort Level ◽  
Hiring Practices ◽  
Related Information ◽  
Different Types ◽  
Prospective Employer ◽  
Types Of Information

The paper examines attitudes towards employers using social media to screen job applicants. In an online survey of 454 participants, we compare the comfort level with this practice in relation to different types of information that can be gathered from publicly accessible social media. The results revealed a nuanced nature of people’s information privacy expectations in the context of hiring practices. People’s perceptions of employers using social media to screen job applicants depends on (1) whether or not they are currently seeking employment (or plan to), (2) the type of information that is being accessed by a prospective employer (if there are on the job market), and (3) their cultural background, but not gender. The findings emphasize the need for employers and recruiters who are relying on social media to screen job applicants to be aware of the types of information that may be perceived to be more sensitive by applicants, such as social network-related information. Keywords : social media, information privacy, job screening, hiring practices

scholarly journals Predictive Analytical Model for Microblogging Data Using Asset Bubble Modelling

2020 ◽  
Vol 14 (2) ◽  
pp. 108-118
Author(s):  
Srinidhi Hiriyannaiah ◽  
Siddesh G.M. ◽  
Srinivasa K.G.
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Predictive Analytics ◽  
Heterogeneous Systems ◽  
Asset Bubble ◽  
Proposed Model ◽  
Different Types ◽  
Social Media Platforms ◽  
Types Of Information ◽  
Better Than

In recent days, social media plays a significant role in the ecosystem of the big data world and its different types of information. There is an emerging need for collection, monitoring, analyzing, and visualizing the different information from various social media platforms in different domains like businesses, public administration, and others. Social media acts as the representative with numerous microblogs for analytics. Predictive analytics of such microblogs provides insights into various aspects of the real-world entities. In this article, a predictive model is proposed using the tweets generated on Twitter social media. The proposed model calculates the potential of a topic in the tweets for the prediction purposes. The experiments were conducted on tweets of the regional election in India and the results are better than the existing systems. In the future, the model can be extended for analysis of information diffusion in heterogeneous systems.

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