scholarly journals Improvement of Misleading and Fake News Classification for Flective Languages by Morphological Group Analysis

Informatics ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 4 ◽  
Author(s):  
Jozef Kapusta ◽  
Juraj Obonya
Keyword(s):  
Language Processing ◽  
Morphological Analysis ◽  
Group Analysis ◽  
Processing Technique ◽  
Fake News ◽  
Sources Of Information ◽  
Morphological Group ◽  
Different Types ◽  
Slovak Language ◽  
Unique Dataset

Due to the constantly evolving social media and different types of sources of information, we are facing different fake news and different types of misinformation. Currently, we are working on a project to identify applicable methods for identifying fake news for floating language types. We explored different approaches to detect fake news in the presented research, which are based on morphological analysis. This is one of the basic components of natural language processing. The aim of the article is to find out whether it is possible to improve the methods of dataset preparation based on morphological analysis. We collected our own and unique dataset, which consisted of articles from verified publishers and articles from news portals that are known as the publishers of fake and misleading news. Articles were in the Slovak language, which belongs to the floating types of languages. We explored different approaches in this article to the dataset preparation based on morphological analysis. The prepared datasets were the input data for creating the classifier of fake and real news. We selected decision trees for classification. The evaluation of the success of two different methods of preparation was carried out because of the success of the created classifier. We found a suitable dataset pre-processing technique by morphological group analysis. This technique could be used for improving fake news classification.

scholarly journals Measuring Adoption of Patient Priorities-Aligned Care Using Natural Language Processing

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 183-183
Author(s):  
Javad Razjouyan ◽  
Jennifer Freytag ◽  
Edward Odom ◽  
Lilian Dindo ◽  
Aanand Naik
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chart Review ◽  
Group Analysis ◽  
Intervention Group ◽  
Multiple Chronic Conditions ◽  
Free Text ◽  
Term Care

Abstract Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults with multiple chronic conditions. Social workers (SW), after online training, document PPC in the patient’s electronic health record (EHR). Our goal is to identify free-text notes with PPC language using a natural language processing (NLP) model and to measure PPC adoption and effect on long term services and support (LTSS) use. Free-text notes from the EHR produced by trained SWs passed through a hybrid NLP model that utilized rule-based and statistical machine learning. NLP accuracy was validated against chart review. Patients who received PPC were propensity matched with patients not receiving PPC (control) on age, gender, BMI, Charlson comorbidity index, facility and SW. The change in LTSS utilization 6-month intervals were compared by groups with univariate analysis. Chart review indicated that 491 notes out of 689 had PPC language and the NLP model reached to precision of 0.85, a recall of 0.90, an F1 of 0.87, and an accuracy of 0.91. Within group analysis shows that intervention group used LTSS 1.8 times more in the 6 months after the encounter compared to 6 months prior. Between group analysis shows that intervention group has significant higher number of LTSS utilization (p=0.012). An automated NLP model can be used to reliably measure the adaptation of PPC by SW. PPC seems to encourage use of LTSS that may delay time to long term care placement.

scholarly journals Morphological, thermal, and thermomechanical properties of cellulose nanocrystals reinforced polylactide/poly [(butylene succinate)-co-adipate] blend composite foams

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Mpho Phillip Motloung ◽  
Simphiwe Zungu ◽  
Vincent Ojijo ◽  
Jayita Bandyopadhyay ◽  
Suprakas Sinha Ray
Keyword(s):  
Morphological Analysis ◽  
Cell Structure ◽  
Environmentally Friendly ◽  
Processing Technique ◽  
Thermomechanical Properties ◽  
Environmental Concerns ◽  
Butylene Succinate ◽  
Peak Intensity ◽  
Composite Foams ◽  
Reinforcing Agent

Abstract This study examines the influence of cellulose nanocrystal (CN) particles on the morphological, thermal, and thermo-mechanical properties of polylactide (PLA)/poly [(butylene succinate)-co-adipate] (PBSA) blend foams prepared by casting and particulate leaching method using fructose as porogen particles. The morphological analysis showed an interconnected open-cell structure, with porosity above 80%. The crystallinity of the prepared foams was disrupted by the inclusion of CN particles as observed from XRD analyses, which showed a decrease in PLA crystal peak intensity. With regards to neat blend foam, the onset thermal degradation increased with the addition of CN particles, which also increased the thermal stability at 50% weight loss. Furthermore, CN acted as a reinforcing agent in improving the stiffness of the prepared blend foam. Overall, completely environmentally friendly foams were successfully prepared, as a potential material that can replace the current existing foam materials that pose many environmental concerns. However, there is a need to develop an environmentally friendly processing technique.

Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

Alliance experience and performance outcomes: A meta-analysis

2021 ◽  
pp. 147612702098287
Author(s):  
Peng Wang ◽  
Xu Jiang ◽  
Maggie Chuoyan Dong
Keyword(s):  
Strategic Alliance ◽  
Meta Analysis ◽  
Group Analysis ◽  
Performance Outcomes ◽  
Country Level ◽  
Different Types ◽  
Performance Consequences ◽  
Controversial Topic ◽  
Alliance Experience ◽  
And Performance

Alliance experience has been a frequent topic in strategic alliance research in recent decades. Nonetheless, its performance consequences, either as a whole or differentiated into general versus partner-specific alliance experience, are neither theoretically clear nor empirically consistent. We use a range of meta-analytic techniques to integrate the empirical findings of 143 studies and provide a more conclusive assessment compared to prior research. Our study thus addresses a long-standing, understudied, and controversial topic: the distinction between the two types of alliance experiences. Going beyond traditional sub-group analysis, we reveal the contextual contingencies by examining how different types of alliance experiences and performance outcomes jointly affect the alliance experience–performance relationship. Moreover, we identify critical country-level institutional contingencies that moderate the focal effect.

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

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