scholarly journals Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors

2021 ◽  
Vol 11 (15) ◽  
pp. 6726
Author(s):  
Xinyu Du ◽  
Limei Ou ◽  
Ye Zhao ◽  
Qi Zhang ◽  
Zongmin Li
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Literature Mining ◽  
Scientific Evaluation ◽  
Governance System ◽  
Governance Effectiveness ◽  
Rational Understanding ◽  
Advanced Analysis ◽  
Massive Information

Social media rumor precise governance is conducive to better coping with the difficulties of rumor monitoring within massive information and improving rumor governance effectiveness. This paper proposes a conceptual framework of social media rumor precise governance system based on literature mining. Accordingly, insightful directions for achieving social media rumor precise governance are introduced, which includes (1) rational understanding of social media rumors, especially large-scale spreading false rumors and recurring false rumors; (2) clear classification of rumor spreaders/believers/refuters/unbelievers; (3) scientific evaluation of rumor governance effectiveness and capabilities. For the above three directions, advanced analysis technologies applications are then summarized. This paper is beneficial to clarify and promote the promising thought of social media rumor precise governance and create impacts on the technologies’ applications in this area.

Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

2021 ◽  
Vol 17 (2) ◽  
pp. 59-78
Author(s):  
Shailendra Kumar Singh ◽  
Manoj Kumar Sachan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Large Scale ◽  
English Language ◽  
Product Reviews ◽  
Internet Users ◽  
Social Media Text ◽  
Analysis System

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.

scholarly journals Countering hate on social media: Large scale classification of hate and counter speech

2020 ◽  
Author(s):  
Joshua Garland ◽  
Keyan Ghazi-Zahedi ◽  
Jean-Gabriel Young ◽  
Laurent Hébert-Dufresne ◽  
Mirta Galesic
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Scale Classification

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

The Swiss Prison Study (SWIPS): Protocol for Establishing a Public Health Registry of Prisoners in Switzerland (Preprint)

2020 ◽  
Author(s):  
Thomas Gaisl ◽  
Naser Musli ◽  
Patrick Baumgartner ◽  
Marc Meier ◽  
Silvana K Rampini ◽  
...  
Keyword(s):  
Public Health ◽  
Medical History ◽  
Large Scale ◽  
International Classification Of Diseases ◽  
Disease Prevalence ◽  
Vulnerable Population ◽  
International Classification ◽  
Classification Of Diseases ◽  
Health Registry

BACKGROUND The health aspects, disease frequencies, and specific health interests of prisoners and refugees are poorly understood. Importantly, access to the health care system is limited for this vulnerable population. There has been no systematic investigation to understand the health issues of inmates in Switzerland. Furthermore, little is known on how recent migration flows in Europe may have affected the health conditions of inmates. OBJECTIVE The Swiss Prison Study (SWIPS) is a large-scale observational study with the aim of establishing a public health registry in northern-central Switzerland. The primary objective is to establish a central database to assess disease prevalence (ie, International Classification of Diseases-10 codes [German modification]) among prisoners. The secondary objectives include the following: (1) to compare the 2015 versus 2020 disease prevalence among inmates against a representative sample from the local resident population, (2) to assess longitudinal changes in disease prevalence from 2015 to 2020 by using cross-sectional medical records from all inmates at the Police Prison Zurich, Switzerland, and (3) to identify unrecognized health problems to prepare successful public health strategies. METHODS Demographic and health-related data such as age, sex, country of origin, duration of imprisonment, medication (including the drug name, brand, dosage, and release), and medical history (including the International Classification of Diseases-10 codes [German modification] for all diagnoses and external results that are part of the medical history in the prison) have been deposited in a central register over a span of 5 years (January 2015 to August 2020). The final cohort is expected to comprise approximately 50,000 to 60,000 prisoners from the Police Prison Zurich, Switzerland. RESULTS This study was approved on August 5, 2019 by the ethical committee of the Canton of Zurich with the registration code KEK-ZH No. 2019-01055 and funded in August 2020 by the “Walter and Gertrud Siegenthaler” foundation and the “Theodor and Ida Herzog-Egli” foundation. This study is registered with the International Standard Randomized Controlled Trial Number registry. Data collection started in August 2019 and results are expected to be published in 2021. Findings will be disseminated through scientific papers as well as presentations and public events. CONCLUSIONS This study will construct a valuable database of information regarding the health of inmates and refugees in Swiss prisons and will act as groundwork for future interventions in this vulnerable population. CLINICALTRIAL ISRCTN registry ISRCTN11714665; http://www.isrctn.com/ISRCTN11714665 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/23973

scholarly journals Public Awareness: What Climate Change Scientists Should Consider

2020 ◽  
Vol 12 (20) ◽  
pp. 8369
Author(s):  
Mohammad Rahimi
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Large Scale ◽  
Public Awareness ◽  
Connected Network ◽  
Multi Scale ◽  
Valuable Addition ◽  
Scientific Approach ◽  
Design Solutions ◽  
Mitigate Climate Change

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

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