scholarly journals Data‐mining social media platforms highlights conservation action for the Mediterranean Critically Endangered blue shark Prionace glauca

2021 ◽  
Author(s):  
Ginevra Boldrocchi ◽  
Tiziano Storai
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Critically Endangered ◽  
Conservation Action ◽  
Blue Shark ◽  
Prionace Glauca ◽  
Social Media Platforms ◽  
The Mediterranean

scholarly journals Machine Learning-Based Data Mining Method for Sentiment Analysis of the Sewol Ferry Disaster's Effect on Social Stress

2020 ◽  
Vol 11 ◽  
Author(s):  
Min-Joon Lee ◽  
Tae-Ro Lee ◽  
Seo-Joon Lee ◽  
Jin-Soo Jang ◽  
Eung Ju Kim
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Stress ◽  
Scientific Evidence ◽  
National Level ◽  
Psychological Impact ◽  
Social Media Platforms ◽  
Negative Sentiment ◽  
Media Data

The Sewol Ferry Disaster which took place in 16th of April, 2014, was a national level disaster in South Korea that caused severe social distress nation-wide. No research at the domestic level thus far has examined the influence of the disaster on social stress through a sentiment analysis of social media data. Data extracted from YouTube, Twitter, and Facebook were used in this study. The population was users who were randomly selected from the aforementioned social media platforms who had posted texts related to the disaster from April 2014 to March 2015. ANOVA was used for statistical comparison between negative, neutral, and positive sentiments under a 95% confidence level. For NLP-based data mining results, bar graph and word cloud analysis as well as analyses of phrases, entities, and queries were implemented. Research results showed a significantly negative sentiment on all social media platforms. This was mainly related to fundamental agents such as ex-president Park and her related political parties and politicians. YouTube, Twitter, and Facebook results showed negative sentiment in phrases (63.5, 69.4, and 58.9%, respectively), entity (81.1, 69.9, and 76.0%, respectively), and query topic (75.0, 85.4, and 75.0%, respectively). All results were statistically significant (p < 0.001). This research provides scientific evidence of the negative psychological impact of the disaster on the Korean population. This study is significant because it is the first research to conduct sentiment analysis of data extracted from the three largest existing social media platforms regarding the issue of the disaster.

scholarly journals Detecting Stress Based on Social Networking Interactions

2019 ◽  
Vol 8 (11) ◽  
pp. 693-696
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Detection Efficiency ◽  
Online Social Network ◽  
Social Characteristics ◽  
Network Information ◽  
Social Media Platforms ◽  
Stress States ◽  
Detection Of Stress ◽  
Social Media Network

Stress is a kind of demand to respond to any in your body's manner. It can be based on experiences that are both good and bad. Psychological stress threatens the health of individuals. People are used to exchanging their schedule and daily operations with colleagues on social media platforms with the reputation of a social media network, creating it possible to hold online social network information for stress detection. For a variety of applications data mining methods are used. Data mining plays a significant role in the detection of stress in sector. We proposed a new model in this article to detect stress. Initially, in this model, discover a correlation between stress states of user and effective public interactions. This describes a set of textual, visual and social characteristics related to stress from different elements and proposes a new hybrid model coupled with Convolutional Neural Network (CNN) to efficiently hold tweet content and data on social interaction to detect stress. The suggested model can enhance the detection efficiency by 97.8 percent, which is quicker than the current scheme, from the experimental outcomes

Choice Modeling of Enterprise Social Media Adoptions

2019 ◽  
Vol 11 (1) ◽  
pp. 12-24
Author(s):  
Chen-Ya Wang ◽  
Hsia-Ching Chang
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Association Rule ◽  
Association Rule Mining ◽  
Fortune 500 ◽  
Econometric Analysis ◽  
Rule Mining ◽  
Enterprise Social Media ◽  
Social Media Platforms ◽  
Dependency Network

To date, many studies focusing on the adoption rates of social media platforms in Fortune 500 firms have been conducted; however, little is known of the adoption time of such platforms, and the relationships between different social media adoptions. This study explores these aspects of social media using a proposed analysis integrating econometric analysis and data mining. Granger causality assists in constructing causal forecasting models of social media adoption time, whereas association rule mining, which can be visualized by dependency network graphs, contributes to understanding hidden relationships among enterprise social media adoption choices. The proposed analysis can account for the unexplained phenomena in a complementary way because different aspects can be drawn from the results of both econometric analysis and data mining.

scholarly journals Genetic differentiation and phylogeography of Mediterranean-North Eastern Atlantic blue shark (Prionace glauca, L. 1758) using mitochondrial DNA: panmixia or complex stock structure?

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4112 ◽  
Author(s):  
Agostino Leone ◽  
Ilenia Urso ◽  
Dimitrios Damalas ◽  
Jann Martinsohn ◽  
Antonella Zanzi ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Atlantic Ocean ◽  
Mediterranean Sea ◽  
Spatial Genetic Structure ◽  
Population Expansion ◽  
Phylogeographic Structure ◽  
Blue Shark ◽  
Prionace Glauca ◽  
North Eastern ◽  
The Mediterranean

BackgroundThe blue shark (Prionace glauca, Linnaeus 1758) is one of the most abundant epipelagic shark inhabiting all the oceans except the poles, including the Mediterranean Sea, but its genetic structure has not been confirmed at basin and interoceanic distances. Past tagging programs in the Atlantic Ocean failed to find evidence of migration of blue sharks between the Mediterranean and the adjacent Atlantic, despite the extreme vagility of the species. Although the high rate of by-catch in the Mediterranean basin, to date no genetic study on Mediterranean blue shark was carried out, which constitutes a significant knowledge gap, considering that this population is classified as “Critically Endangered”, unlike its open-ocean counterpart.MethodsBlue shark phylogeography and demography in the Mediterranean Sea and North-Eastern Atlantic Ocean were inferred using two mitochondrial genes (Cytb and control region) amplified from 207 and 170 individuals respectively, collected from six localities across the Mediterranean and two from the North-Eastern Atlantic.ResultsAlthough no obvious pattern of geographical differentiation was apparent from the haplotype network, Φst analyses indicated significant genetic structure among four geographical groups. Demographic analyses suggest that these populations have experienced a constant population expansion in the last 0.4–0.1 million of years.DiscussionThe weak, but significant, differences in Mediterranean and adjacent North-eastern Atlantic blue sharks revealed a complex phylogeographic structure, which appears to reject the assumption of panmixia across the study area, but also supports a certain degree of population connectivity across the Strait of Gibraltar, despite the lack of evidence of migratory movements observed by tagging data. Analyses of spatial genetic structure in relation to sex-ratio and size could indicate some level of sex/stage biased migratory behaviour.

Biological characteristics of blue shark, Prionace glauca, in the Mediterranean Sea

2009 ◽  
Vol 89 (6) ◽  
pp. 1233-1242 ◽  
Author(s):  
Persefoni Megalofonou ◽  
Dimitris Damalas ◽  
Gregorio de Metrio
Keyword(s):  
Sex Ratio ◽  
Mediterranean Sea ◽  
Oocyte Diameter ◽  
Longline Fishery ◽  
Blue Shark ◽  
Prionace Glauca ◽  
Ovary Weight ◽  
Caudal Vertebrae ◽  
The Mediterranean ◽  
Age Estimates

A total of 870 blue sharks ranging from 70 to 349 cm in total length (LT) were sampled from the swordfish longline fishery in the Mediterranean Sea during the period 1998–2003. Males predominated and the sex-ratio (1:1.8) showed an increase in the proportion of males as size of fish increased. Gonad observation revealed that females smaller than 120 cm LT had immature ovaries with no mature oocytes, while mature ovaries with visible yolky oocytes were present in specimens larger than 203 cm LT. Ovary weight varied from 4–137 g and maximum oocyte diameter was 21.1 mm in mature females. All males smaller than 125 cm LT were immature while males larger than 187 cm LT were mature, as indicated by the presence of heavily calcified claspers, which extended beyond the posterior end of the pelvic fins. Length at 50% maturity (L50) was estimated to be 202.9 cm LT for males and 214.7 cm LT for females. Age estimates using caudal vertebrae ranged from 1 to 12 years, while age at 50% maturity was estimated at 4.9 years for males and 5.5 year for females.

scholarly journals Data Mining for Cyberbullying and Harassment Detection in Arabic Texts

2021 ◽  
Vol 13 (5) ◽  
pp. 41-50
Author(s):  
Eman Bashir ◽  
◽  
Mohamed Bouguessa
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Deep Learning ◽  
Young People ◽  
Arabic Text ◽  
Learning Approaches ◽  
Learning Models ◽  
Social Media Platforms ◽  
Arabic World

Broadly cyberbullying is viewed as a severe social danger that influences many individuals around the globe, particularly young people and teenagers. The Arabic world has embraced technology and continues using it in different ways to communicate inside social media platforms. However, the Arabic text has drawbacks for its complexity, challenges, and scarcity of its resources. This paper investigates several questions related to the content of how to protect an Arabic text from cyberbullying/harassment through the information posted on Twitter. To answer this question, we collected the Arab corpus covering the topics with specific words, which will explain in detail. We devised experiments in which we investigated several learning approaches. Our results suggest that deep learning models like LSTM achieve better performance compared to other traditional yberbullying classifiers with an accuracy of 72%.

Twitter Data Mining for Situational Awareness

2019 ◽  
pp. 684-695
Author(s):  
Marco Vernier ◽  
Manuela Farinosi ◽  
Gian Luca Foresti
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Emergency Management ◽  
Situational Awareness ◽  
Great Majority ◽  
Learning Systems ◽  
Haiti Earthquake ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Self Learning

The most recent catastrophic events, from the 2010 Haiti earthquake to the devastating 2013 Colorado floods, have shown a strong adoption of social media platforms by ordinary people. The data and metadata produced by the users during and after the extraordinary situations could have enormous potentialities if integrated with the traditional systems for emergency management and used for hyperlocal situational awareness. The great majority of the current literature is focused on Twitter for several reasons strictly linked to the architectures and practices of use of the platform itself. It is possible to classify the existing systems based on the analysis of Twitter data at least in three different categories: 1) semantic systems, 2) metadata systems, and 3) smart self-learning systems. In this chapter, a review of the most significant and important tools used to analyze Twitter data will be presented and an innovative and smart solution will be proposed for future development.

scholarly journals The impact of Location-based Marketing strategies on retail sectors in Al Batinah region during COVID 19 Pandemic

2021 ◽  
Vol 7 (2) ◽  
pp. 07-17
Author(s):  
Shantanu Shekhar ◽  
Kumaresan Chandrasekaran ◽  
Joshy Mathew
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Exploratory Study ◽  
Marketing Strategies ◽  
Retail Sector ◽  
Sultanate Of Oman ◽  
Medium Scale ◽  
Social Media Platforms ◽  
The Right ◽  
The Impact

Purpose: Location-based marketing has become an essential component in today’s businesses. The principal objective of this study is to investigate the growing significance of using location-based marketing services to small and medium-scale marketers and customers. Methodology: This is an exploratory study, which aims to explore the impact of location-based marketing on the retail sector of the Al Batinah region in the Sultanate of Oman. Main Findings: Social media platforms play an important role in the Location-Based marketing of various retail sectors such as SME’s and these platforms play a major role in helping retailers to make decisions whether they wish to market their products by using this platform or not. Implications: All organizations’ top-level management must focus on data mining to identify the right customers and at the same time they should focus on innovative marketing strategies on LBS. Novelty: This study provides motivations and insights that drives businesses and customers to use these LBM services.

scholarly journals Online News Feed Data Mining and Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 409-414
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Online News ◽  
Data Set ◽  
The Social ◽  
News Agencies ◽  
Social Media Platforms ◽  
Prediction Systems ◽  
Prediction Techniques ◽  
Different Sources

Data mining and prediction systems have been the center of attraction since information retrieval came into existence. Most IT companies spend a lot of resources on such analysis and systems to improve their performance and generate more revenue depending on the nature of work that they do. Online News Feed Prediction System aims to provide an analysis and comparison of various prediction techniques by using different methods of implementation. UCI repository contains a collection of databases pertaining to different topics. News popularity in multiple social media is one such dataset containing information about news topics from different sources, sentiment analysis of title and headline, topic that they are related to, publishing date, popularity score in various social media platforms. Python, R and Weka have been used on this data set to implement data preprocessing, visualization and prediction techniques like Random Forest, Decision Tree and SVM. Moreover, there is dataset on the analysis of the score for every twenty minutes for the social media platforms chosen. Analysis on these platforms helps in developing a system to reach a wider audience. News agencies can use this system to increase their profit and visibility. This paper aims to realize the ways to obtain these results

scholarly journals Data mining on YouTube reveals fisher group-specific harvesting patterns and social engagement in recreational anglers and spearfishers

2019 ◽  
Author(s):  
Valerio Sbragaglia ◽  
Ricardo A. Correia ◽  
Salvatore Coco ◽  
Robert Arlinghaus
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Engagement ◽  
Seasonal Pattern ◽  
Conservation Status ◽  
Recreational Fisheries ◽  
Average Mass ◽  
Recreational Angling ◽  
Social Media Platforms ◽  
Dentex Dentex

Data about recreational fisheries are scarce in many areas of the world. In the absence of monitoring data collected in situ, alternative data sources, such as digital applications and social media platforms, have the potential to produce valuable insights. Yet, the potential of social media for drawing insights about recreational fisheries is still underexplored. In this study, we applied data mining on YouTube videos to better understand recreational fisheries targeting common dentex (Dentex dentex), an iconic species of Mediterranean recreational fisheries. We chose this model species because of ongoing controversies about the relative impact of recreational angling and recreational spearfishing on its conservation status. In Italy alone, from 2010 to 2016 recreational spearfishers posted 1051 videos compared to 692 videos posted by recreational anglers. Only the upload pattern of spearfishing videos followed a seasonal pattern with peaks in July, suggesting seasonality of spearfishing catches of D. dentex – a trend not found for anglers. The average mass of the fish declared in recreational angling videos (6.43 kg) was significantly larger than the one in spearfishing videos (4.50 kg). Videos posted by recreational spearfishers received significantly more likes and comments than those posted by recreational anglers, suggesting that the social engagement among recreational spearfishers was stronger than in anglers. We also found that the mass of the fish positively predicted social engagement in recreational spearfishing videos, but not in videos posted by recreational anglers. This could be caused by the generally smaller odds of catching large D. dentex by spearfishing, possibly explaining why posting videos with particularly large specimen triggered larger social engagement by recreational spearfishers. Our case study demonstrates that data mining on YouTube can be a powerful tool to provide complementary data on controversial and data-poor aspects of recreational fisheries.

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