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 Delivering open innovation promises through social media

2018 ◽  
Vol 39 (6) ◽  
pp. 21-28 ◽  
Author(s):  
Pierre-Jean Barlatier ◽  
Emmanuel Josserand
Keyword(s):  
Social Media ◽  
Open Innovation ◽  
Success Factors ◽  
Single Case ◽  
Low Cost ◽  
Energy Transition ◽  
Full Potential ◽  
Single Case Study ◽  
Content Type

Purpose This paper aims to explore how social media can be used strategically for delivering the promises of open innovation and examines the types of structure that can foster the integration of these new tools with more classic top-down innovation approaches. Design/methodology/approach A single case study of, ALPHA (pseudonym), a multinational company that combined an integrated strategy and the creation of a lean structure with the full potential of social media. Findings To take on the challenges of energy transition, ALPHA has implemented a low-cost approach allowing it to harness the promises of open innovation. This combined the introduction of a lean structure, two social media platforms and processes that ensured the integration of open innovation activities with existing departments. Research limitations/implications The research is based on a single case study. Further research should be conducted to establish the generalization of the results. Practical implications This paper highlights the key success factors in making such a light approach successful, namely, controlling cost and disruption of open innovation; integration matters; leveraging complementarities with existing social media initiatives; and bottom-up adoption. Originality/value The research provides a unique approach that can be practically implemented to leverage social media to deliver the promises of open innovation and offers an original way of integrating social media lead innovation and open innovation strategies with more classic R&D activities.

Design and Selection of Safe Water Supply Solutions for Emerging Regions: A Demography Based Demand Driven Approach

2012 ◽  
Author(s):  
Shaiful Arif ◽  
Zahed Siddique
Keyword(s):  
Drinking Water ◽  
Global Climate ◽  
Low Cost ◽  
Source Model ◽  
Efficient Design ◽  
Rural Village ◽  
Treatment Processes ◽  
Design Alternatives ◽  
Selection Of

Due to global climate change, increase in pollution along with reduced quantity of drinking water compared to the total volume of water, the scarcity of potable water is declining gradually. Researchers have become increasingly interested in efficient design of treatment processes, but, there is a lack of research to investigate appropriate, applicable, low cost and simple water treatment processes for underprivileged communities. Providing safe drinking water in these communities is more challenging due to limitation of resources and infrastructure. In this paper we developed a mathematical foundation of Demography Based Demand Driven (DBDD) approach to capture and identify design alternatives (combination of different treatment processes). The developed approach assists to identify, extract, categorize, and compare water related attributes associated with a community and mapped onto source model to identify and select a set of feasible treatment processes. A case study for a community of a rural village in emerging regions of Honduras is modeled and the approach presented in this paper is implemented to design and select feasible service solutions.

Self-Powering Gyms: A Case Study on Energy Harvesting From a Static Bicycle

2019 ◽  
Author(s):  
Mustafa Ihsan ◽  
Vimal Viswanathan
Keyword(s):  
Energy Harvesting ◽  
Energy Efficient ◽  
Low Voltage ◽  
Global Climate ◽  
Low Cost ◽  
Electrical Circuit ◽  
Green Energy ◽  
Global Climate Changes ◽  
In Series

Abstract The increasing levels of pollution and global climate changes have spurred growing interest in harvesting green energy from all possible resources. One of the under-utilized sources is the energy that one spends during physical exercise at gymnasiums. If the energy that a person expends can be harvested, that may suffice to power the facility at least partially. This paper describes the research, development, and execution of a low-cost arrangement to harvest energy from a static bicycle at a gym. Primarily, the setup uses a generator attached to the bicycle to produce low-voltage electricity. Further, an electrical circuit is designed and implemented to amplify the voltage and send it to a 585CCA battery. The resulting arrangement is found to be sufficient to completely charge the car battery with 12–15 hours of riding of one bicycle. It is estimated that this battery can power two energy-efficient lamps for around 13 hours. In other words, a simple setup attached to various cardio equipment in series may be sufficient to power the gym partially. Further, an economic analysis is conducted to estimate the energy saving resulting from the implementation of the energy harvesting arrangement in a college gym. It is found that with the help of such an arrangement, approximately 20% of the energy cost of the gym room can be saved.

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.

scholarly journals Social Media Marketing Adoption Strategies: An Exploratory Multiple Case Study of Landscape Industry Small Businesses

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Crystal Lupo ◽  
Jason R. Stroman
Keyword(s):  
Social Media ◽  
Small Business ◽  
Small Businesses ◽  
Low Cost ◽  
Business Owners ◽  
Marketing Strategies ◽  
Social Media Marketing ◽  
Small Business Owners ◽  
Multiple Case

Despite research contending that marketing is a pivotal factor in small business success, many small business owners continue to underutilize low-cost marketing options available to them. Of these options, social media marketing is a useful tool to maintain competitiveness in the larger marketplace. However, the adoption of social media best practices in small business remains deficient. The landscape industry is a large and growing field with small businesses making up a large and growing share of the industry. Yet some landscape industry small business owners lack strategies to adopt innovative social media marketing strategies to help ensure business viability. This study incorporated a qualitative, exploratory multiple-case study design to explore how landscape industry small business owners use social media marketing strategies to help ensure business viability. Results indicated that successful marketing strategies tended to incorporate Facebook as the primary social media tool and included content such as service, education, and holiday posts. Benefits of social media marketing primarily centered on low-cost marketing options for improved visibility, while challenges included a trial-and-error learning curve. Results of this study might help small businesses improve their long-term viability through social media marketing strategies.

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.

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.

Notes: Social media firestorms and the protection of smaller enterprises: Ubuntu Baba and Woolworths

2021 ◽  
Vol 138 (4) ◽  
pp. 715-731
Author(s):  
Peter Kituri ◽  
Andrew Hutchison ◽  
James Lappeman
Keyword(s):  
Social Media ◽  
South African ◽  
Small Businesses ◽  
Low Cost ◽  
Small Enterprises ◽  
Corporate Power ◽  
Legal Sanction ◽  
The Face ◽  
Use Of Social Media

In this note we explore the use of social media as a tool to help small enterprises exert pressure on large corporations. Specifically, we use the case study of a small South African business (Ubuntu Baba) that exerted a powerful non-legal sanction on major retailer Woolworths through social media. This entrepreneur-initiated social media firestorm led to victory in the court of public opinion and a quick settlement. This low-cost option was possibly chosen in the face of the costs and uncertainties of more conventional legal recourse. Small businesses are an important component of the South African government’s strategy for economic development and employment creation, yet the existing laws protecting weaker parties often leave small businesses exposed to corporate power-play. This form of corporate power imbalance is a core theme underlying our case study.

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