scholarly journals Crowdsourcing and Machine Learning Approaches for Extracting Entities Indicating Potential Foodborne Outbreaks From Social Media

2021 ◽  
Author(s):  
Dandan Tao ◽  
Dongyu Zhang ◽  
Ruofan Hu ◽  
Elke Rundensteiner ◽  
Hao Feng
Keyword(s):  
Social Media ◽  
Economic Loss ◽  
Consumer Confidence ◽  
Learning Approaches ◽  
Entity Extraction ◽  
Foodborne Illnesses ◽  
Loss Of Life ◽  
Foodborne Outbreaks ◽  
Significant Economic Loss

Abstract Foodborne outbreaks are a serious but preventable threat to public health that often lead to illness, loss of life, significant economic loss, and the erosion of consumer confidence. Understanding how consumers respond when interacting with foods, as well as extracting information from posts on social media may provide new means of reducing the risks and curtailing the outbreaks. In recent years, Twitter has been employed as a new tool for identifying unreported foodborne illnesses. However, there is a huge gap between the identification of sporadic illnesses and the early detection of a potential outbreak. In this work, the dual-task BERTweet model was developed to identify unreported foodborne illnesses and extract foodborne-illness-related entities from Twitter. Unlike previous methods, our model leveraged the mutually beneficial relationships between the two tasks. The results showed that the F1-score of relevance prediction was 0.87, and the F1-score of entity extraction was 0.61. Key elements such as time, location, and food detected from sentences indicating foodborne illnesses were used to analyze potential foodborne outbreaks in massive historical tweets. A case study on tweets indicating foodborne illnesses showed that the discovered trend is consistent with the true outbreaks that occurred during the same period.

scholarly journals Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dandan Tao ◽  
Dongyu Zhang ◽  
Ruofan Hu ◽  
Elke Rundensteiner ◽  
Hao Feng
Keyword(s):  
Social Media ◽  
Economic Loss ◽  
Consumer Confidence ◽  
Learning Approaches ◽  
Entity Extraction ◽  
Foodborne Illnesses ◽  
Loss Of Life ◽  
Foodborne Outbreaks ◽  
Significant Economic Loss

AbstractFoodborne outbreaks are a serious but preventable threat to public health that often lead to illness, loss of life, significant economic loss, and the erosion of consumer confidence. Understanding how consumers respond when interacting with foods, as well as extracting information from posts on social media may provide new means of reducing the risks and curtailing the outbreaks. In recent years, Twitter has been employed as a new tool for identifying unreported foodborne illnesses. However, there is a huge gap between the identification of sporadic illnesses and the early detection of a potential outbreak. In this work, the dual-task BERTweet model was developed to identify unreported foodborne illnesses and extract foodborne-illness-related entities from Twitter. Unlike previous methods, our model leveraged the mutually beneficial relationships between the two tasks. The results showed that the F1-score of relevance prediction was 0.87, and the F1-score of entity extraction was 0.61. Key elements such as time, location, and food detected from sentences indicating foodborne illnesses were used to analyze potential foodborne outbreaks in massive historical tweets. A case study on tweets indicating foodborne illnesses showed that the discovered trend is consistent with the true outbreaks that occurred during the same period.

Reports From the Field: “Vini Ansanm” Come Together for Inclusive Community Music Development in Port-au-Prince, Haiti

2020 ◽  
pp. 273-289
Author(s):  
Donald DeVito ◽  
Gertrude Bien-Aime ◽  
Hannah Ehrli ◽  
Jamie Schumacher
Keyword(s):  
Social Media ◽  
Inclusive Education ◽  
Children With Disabilities ◽  
Small Population ◽  
Significant Loss ◽  
Community Music ◽  
Music Learning ◽  
Loss Of Life

Haiti has experienced a series of catastrophic natural disasters in recent decades, resulting in significant loss of life and long-term damage to infrastructure. One critical outcome of these disasters is that there are approximately 400,000 orphans in the small population of just over 10 million. Throughout Haiti, children with disabilities are often considered cursed, and thus are rejected by the community in which they live. Haitian children with disabilities need creative and educational activities that will help them grow, develop, enjoy their lives, and become accepted members of the community. This chapter on the Haitian Center for Inclusive Education presents a case study of social media engagement and music learning, with an emphasis on social justice that has contributed to sustainable efforts.

scholarly journals Conflict and chaos: A case study of Ireland’s COVID-19 outbound travel market

2021 ◽  
Vol 35 (3) ◽  
pp. 87-108
Author(s):  
Tony Johnston
Keyword(s):  
Public Health ◽  
Social Media ◽  
Crisis Management ◽  
Consumer Confidence ◽  
Operational Decisions ◽  
Societal Factors ◽  
Mainstream Media ◽  
Travel Industry ◽  
Desk Research

During the COVID-19 pandemic the international outbound travel market from Ireland collapsed, declining at one point by 94%. This case study paper explores the environment which framed the collapse in travel, positioning it as one of conflict and chaos. The main objective is to document and analyse the legal, industry and societal factors which may have contributed to the collapse, identifying the key regulations, decisions, metrics, and societal responses, and exploring their intersection with outbound tourism. Three areas of inquiry are explored, namely: 1) the legal instruments used by government to restrict travel, 2) operational decisions made by industry, and 3) societal and media response to the pandemic. Three findings are presented from the desk research. First, it is suggested that the conflicting agendas of government and public health, the mainstream media and the travel industry would be more effectively dealt with in private as opposed to via news articles, social media arguments, and openly published letters. Second, clarity of communication from all three bodies needs improvement due to its impact on consumer confidence. Finally, the article proposes lessons for government in relation to future crisis management situations regarding outbound travel.

CID: Categorical Influencer Detection on microtext-based social media

2020 ◽  
Vol 44 (5) ◽  
pp. 1027-1055
Author(s):  
Thanh-Tho Quan ◽  
Duc-Trung Mai ◽  
Thanh-Duy Tran
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Learning Approaches ◽  
Content Type ◽  
Modeling Process ◽  
Variational Autoencoder ◽  
Media Channels

PurposeThis paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.Design/methodology/approachWe deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.FindingsThe approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.Research limitations/implicationsThis work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.Practical implicationsThis work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.Originality/valueIn this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

Deep Learning in Social Media Analysis

2021 ◽  
pp. 164-194
Author(s):  
Vaishali Yogesh Baviskar ◽  
Rachna Yogesh Sable
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Media Analysis ◽  
Network Services ◽  
Social Media Analytics ◽  
Learning Approaches ◽  
Election Result ◽  
The Social ◽  
Social Network Services

Social media analytics keep on collecting the information from different media platforms and then calculating the statistical data. Twitter is one of the social network services which has ample amount of data where many users used post significant amounts of data on a regular basis. Handling such a large amount of data using traditional tools and technologies is very complicated. One of the solutions to this problem is the use of machine learning and deep learning approaches. In this chapter, the authors present a case study showing the use of Twitter data for predicting the election result of the political parties.

scholarly journals METEOTSUNAMI RISK AND THE USE OF DYNAMIC MOORING ANALYSIS AND SHORETENSION TO MITIGATE RISK IN FREMANTLE PORT

2020 ◽  
pp. 27
Author(s):  
Rohan Hudson ◽  
Justin Cross
Keyword(s):  
Sea Level ◽  
Gravity Waves ◽  
Economic Loss ◽  
Technical Solution ◽  
Railway Bridge ◽  
Loss Of Life ◽  
Railway Line ◽  
Wind Gusts ◽  
Atmospheric Gravity ◽  
Significant Economic Loss

Meteotsunamis are generated by meteorological events, particularly moving pressure disturbances due to squalls, thunderstorms, frontal passages and atmospheric gravity waves. Relatively small initial sea-level perturbations, of the order of a few centimetres, can increase significantly through multi-resonant phenomena to create tsunami like destructive events resulting in injury, loss of life, damage to infrastructure and significant economic loss. On the 17th August 2014, severe metocean conditions (including 27.8 m/s wind gusts and a meteotsunami) resulted in the vessels Grand Pioneer and AAL Fremantle breaking loose from their mooring at Berth 11 and 12 in Fremantle Port. One of the vessels collided with a railway bridge closing the commuter railway line for two weeks. Royal HaskoningDHV was commissioned by Fremantle Ports to undertake a hydrodynamic investigation and a dynamic mooring analysis (DMA) to determine the cause of the event and provide a technical solution to provide safe moorings in the port.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/idTLjfeajiM

STRATEGI KOMUNIKASI CROWDFUNDING PADA KOLASE.COM

2019 ◽  
Vol 2 (2) ◽  
pp. 177-187
Author(s):  
Venessa Agusta Gogali ◽  
Fajar Muharam ◽  
Syarif Fitri
Keyword(s):  
Social Media ◽  
Communication Strategies ◽  
Communication Strategy ◽  
New Method ◽  
Media Communication ◽  
Social Activists ◽  
Social Media Communication

Crowdfunding is a new method in fundraising activities based online. Moreover, the level of penetration of social media to the community is increasingly high. This makes social activists and academics realize that it is important to study social media communication strategies in crowdfunding activities. There is encouragement to provide an overview of crowdfunding activities. So the author conducted a research on "Crowdfunding Communication Strategy Through Kolase.com Through Case Study on the #BikinNyata Program Through the Kolase.com Website that successfully achieved the target. Keywords: Strategic of Communication, Crowdfunding, Social Media.

Marketing communication objectives through digital content marketing on social media

2020 ◽  
pp. 79-104
Author(s):  
Janice J. Nieves-Casasnovas ◽  
Frank Lozada-Contreras
Keyword(s):  
Social Media ◽  
Marketing Strategies ◽  
Brand Awareness ◽  
Digital Content ◽  
Media Content ◽  
Marketing Communication ◽  
Case Study Design ◽  
Multiple Case ◽  
Content Marketing

The purpose of this study was to determine what type of marketing communication objectives are present in the digital content marketing developed by luxury auto brands with social media presence in Puerto Rico, particularly Facebook. A longitudinal multiple-case study design was used to analyze five luxury auto brands using content analysis on Facebook posts. This analysis included identification of marketing communication objectives through social media content marketing strategies, type of media content and social media metrics. Our results showed that the most used objectives are brand awareness, brand personality, and brand salience. Another significant result is that digital content marketing used by brands in social media are focused towards becoming more visible and recognized; also, reflecting human-like traits and attitudes in their social media.

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