scholarly journals On How Crowdsourced Data and Landscape Organisation Metrics Can Facilitate the Mapping of Cultural Ecosystem Services: An Estonian Case Study

Land ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 158 ◽  
Author(s):  
Oleksandr Karasov ◽  
Stien Heremans ◽  
Mart Külvik ◽  
Artem Domnich ◽  
Igor Chervanyov
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Outdoor Recreation ◽  
Daily Lives ◽  
Machine Learning Classification ◽  
Viewshed Analysis ◽  
Crowdsourced Data ◽  
Automated Machine Learning ◽  
Cultural Ecosystem Service

Social media continues to grow, permanently capturing our digital footprint in the form of texts, photographs, and videos, thereby reflecting our daily lives. Therefore, recent studies are increasingly recognising passively crowdsourced geotagged photographs retrieved from location-based social media as suitable data for quantitative mapping and assessment of cultural ecosystem service (CES) flow. In this study, we attempt to improve CES mapping from geotagged photographs by combining natural language processing, i.e., topic modelling and automated machine learning classification. Our study focuses on three main groups of CESs that are abundant in outdoor social media data: landscape watching, active outdoor recreation, and wildlife watching. Moreover, by means of a comparative viewshed analysis, we compare the geographic information system- and remote sensing-based landscape organisation metrics related to landscape coherence and colour harmony. We observed the spatial distribution of CESs in Estonia and confirmed that colour harmony indices are more strongly associated with landscape watching and outdoor recreation, while landscape coherence is more associated with wildlife watching. Both CES use and values of landscape organisation indices are land cover-specific. The suggested methodology can significantly improve the state-of-the-art with regard to CES mapping from geotagged photographs, and it is therefore particularly relevant for monitoring landscape sustainability.

The Neuroscience of Social Television

2018 ◽  
pp. 439-452
Author(s):  
Shaun A Seixas ◽  
Geoffrey E Nield ◽  
Peter Pynta ◽  
Richard B Silberstein
Keyword(s):  
Social Media ◽  
Opposite Effect ◽  
Negative Impact ◽  
Consumer Engagement ◽  
Daily Lives ◽  
The Way

In a short few years, social media has become the dominant way in which we communicate with the outside world. It has become prevalent in almost every aspect of our daily lives, but one of the most significant changes social media has had, has been on the way we watch television. This phenomenon, known as dual screening, has caused some concern amongst marketers and advertisers, who believed that this behaviour was having an overall negative impact on consumer engagement with television. This chapter attempts to address some of these concerns by providing evidence obtained from the neurosciences and from a case study. The evidence we present in this chapter demonstrates the opposite effect, whereby social media can actually be used to enhance viewer engagement.

Traffic Impact Area Detection and Spatiotemporal Influence Assessment for Disaster Reduction Based on Social Media: A Case Study of the 2018 Beijing Rainstorm

2020 ◽  
Vol 9 (2) ◽  
pp. 136
Author(s):  
Tengfei Yang ◽  
Jibo Xie ◽  
Guoqing Li ◽  
Naixia Mou ◽  
Cuiju Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Global Climate ◽  
Low Cost ◽  
Disaster Reduction ◽  
Emotional Information ◽  
Road Condition ◽  
Traffic Impact ◽  
Disaster Data

The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper.

scholarly journals FRIDZ: A Framework for Real-time Identification of Disaster Zones

2019 ◽  
Author(s):  
Abhisek Chowdhury
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Semantic Analysis ◽  
Flood Events ◽  
Catastrophic Event ◽  
Real Time Identification ◽  
Potential Disaster ◽  
Extract Information

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of geographic information. Among which Twitter, a popular micro-blogging service, has recently gained tremendous attention for its real-time nature. For instance, during floods, people usually tweet which enable detection of flood events by observing the twitter feeds promptly. In this paper, we propose a framework to investigate the real-time interplay between catastrophic event and peo-ples’ reaction such as flood and tweets to identify disaster zones. We have demonstrated our approach using the tweets following a flood in the state of Bihar in India during year 2017 as a case study. We construct a classifier for semantic analysis of the tweets in order to classify them into flood and non-flood categories. Subsequently, we apply natural language processing methods to extract information on flood affected areas and use elevation maps to identify potential disaster zones.

scholarly journals Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

2019 ◽  
Vol 8 (10) ◽  
pp. 3429-3434 ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Sentiment Classification ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product’s. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer’s mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, Decision Tree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.

The Neuroscience of Social Television

2015 ◽  
pp. 153-166
Author(s):  
Shaun A Seixas ◽  
Geoffrey E Nield ◽  
Peter Pynta ◽  
Richard B Silberstein
Keyword(s):  
Social Media ◽  
Opposite Effect ◽  
Negative Impact ◽  
Consumer Engagement ◽  
Daily Lives ◽  
The Way

In a short few years, social media has become the dominant way in which we communicate with the outside world. It has become prevalent in almost every aspect of our daily lives, but one of the most significant changes social media has had, has been on the way we watch television. This phenomenon, known as dual screening, has caused some concern amongst marketers and advertisers, who believed that this behaviour was having an overall negative impact on consumer engagement with television. This chapter attempts to address some of these concerns by providing evidence obtained from the neurosciences and from a case study. The evidence we present in this chapter demonstrates the opposite effect, whereby social media can actually be used to enhance viewer engagement.

scholarly journals Using sentiment analysis to predict opinion inversion in Tweets of political communication

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yogev Matalon ◽  
Ofir Magdaci ◽  
Adam Almozlino ◽  
Dan Yamin
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Natural Language Processing ◽  
Random Forest ◽  
Political Communication ◽  
Language Processing ◽  
Source Text ◽  
Social Media Networks ◽  
Contrasting View

AbstractSocial media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the original author. Using politically-oriented discourse related to Israel with focus on the Israeli–Palestinian conflict, we explored this Opinion Inversion (O.I.) phenomenon. From a corpus of approximately 716,000 relevant Tweets, we identified 7147 Source–Quote pairs. These Source–Quote pairs accounted for 69% of the total volume of the corpus. Using a Random Forest model based on the Natural Language Processing features of the Source text and user attributes, we could predict whether a Source will undergo O.I. upon retweet with an ROC-AUC of 0.83. We found that roughly 80% of the factors that explain O.I. are associated with the original message's sentiment towards the conflict. In addition, we identified pairs comprised of Quotes related to the domain while their Sources were unrelated to the domain. These Quotes, which accounted for 14% of the Source–Quote pairs, maintained similar sentiment levels as the Source. Our case study underscores that O.I. plays an important role in political communication on social media. Nevertheless, O.I. can be predicted in advance using simple artificial intelligence tools and that prediction might be used to optimize content propagation.

The Neuroscience of Social Television

2018 ◽  
pp. 413-426
Author(s):  
Shaun A Seixas ◽  
Geoffrey E Nield ◽  
Peter Pynta ◽  
Richard B Silberstein
Keyword(s):  
Social Media ◽  
Opposite Effect ◽  
Negative Impact ◽  
Consumer Engagement ◽  
Daily Lives ◽  
The Way

In a short few years, social media has become the dominant way in which we communicate with the outside world. It has become prevalent in almost every aspect of our daily lives, but one of the most significant changes social media has had, has been on the way we watch television. This phenomenon, known as dual screening, has caused some concern amongst marketers and advertisers, who believed that this behaviour was having an overall negative impact on consumer engagement with television. This chapter attempts to address some of these concerns by providing evidence obtained from the neurosciences and from a case study. The evidence we present in this chapter demonstrates the opposite effect, whereby social media can actually be used to enhance viewer engagement.

scholarly journals Lexicon based Chinese language sentiment analysis method

2019 ◽  
Vol 16 (2) ◽  
pp. 639-655
Author(s):  
Jinyan Chen ◽  
Susanne Becken ◽  
Bela Stantic
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Chinese Language ◽  
Big Data Analytics ◽  
Limited Attention ◽  
Analysis Method ◽  
Media Platform ◽  
Chinese Social Media

The growing number of social media users and vast volume of posts could provide valuable information about the sentiment toward different locations, services as well as people. Recent advances in Big Data analytics and natural language processing often means to automatically calculate sentiment in these posts. Sentiment analysis is challenging and computationally demanding task due to the volume of data, misspelling, emoticons as well as abbreviations. While significant work was directed toward the sentiment analysis of English text there is limited attention in literature toward the sentiment analytic of Chinese language. In this work we propose method to identify the sentiment in Chinese social media posts and to test our method we rely on posts sent by visitors of Great Barrier Reef by users of most popular Chinese social media platform Sina Weibo. We elaborate process of capturing of weibo posts, describe a creation of lexicon as well as develop and explain algorithm for sentiment calculation. In case study, related to sentiment toward the different GBR destinations, we demonstrate that the proposed method is effective in obtaining the information and is suitable to monitor visitors? opinion.

Designing DMO Communication Strategies Through the Use of a Step-by-Step Model

2021 ◽  
pp. 306-330
Author(s):  
Vitor Roque ◽  
Rui Raposo
Keyword(s):  
Social Media ◽  
Modern Society ◽  
The Other ◽  
The Internet ◽  
Daily Lives ◽  
Internet Connection ◽  
Step Model ◽  
Use Of Social Media ◽  
Destination Management Organizations

Nowadays, the internet, social media, mobile devices, and other equipment with an internet connection play a crucial role in our daily lives and the complex networks of modern society. Destination management organizations (DMO) must regard social media as essential tools for improving their competitiveness through, for instance, engagement to extract and understand customer behaviors and needs. The question is, how may DMO tackle the challenge of bringing social media into their communication plans and strategies? With this challenge in mind, a model was designed and tested to contribute to the DMO's goal of integrating and enhancing the use of social media in their communication and promotion-related activities. The model presented in this chapter is partially the result of two questionnaires. One applied to travelers, and the other was used with DMO and in the observation of the usage of several DMO social media accounts; and a case study was developed in cooperation with a local DMO.

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