scholarly journals Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 213154-213167
Author(s):  
Giuseppe Sansonetti ◽  
Fabio Gasparetti ◽  
Giuseppe D'aniello ◽  
Alessandro Micarelli
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Automatic Detection ◽  
Learning Techniques

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

scholarly journals SENTIMENT ANALYSIS FOR SARCASTIC MESSAGES IN SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES - AN EMPIRICAL STUDY

2021 ◽  
Vol 9 (2) ◽  
pp. 1051-1052
Author(s):  
K. Kavitha, Et. al.
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Empirical Study ◽  
Sentiment Analysis ◽  
Learning Strategies ◽  
Language Processing ◽  
The People ◽  
Learning Techniques ◽  
Key Terms ◽  
Learning Frameworks

Sentiments is the term of opinion or views about any topic expressed by the people through a source of communication. Nowadays social media is an effective platform for people to communicate and it generates huge amount of unstructured details every day. It is essential for any business organization in the current era to process and analyse the sentiments by using machine learning and Natural Language Processing (NLP) strategies. Even though in recent times the deep learning strategies are becoming more familiar due to higher capabilities of performance. This paper represents an empirical study of an application of deep learning techniques in Sentiment Analysis (SA) for sarcastic messages and their increasing scope in real time. Taxonomy of the sentiment analysis in recent times and their key terms are also been highlighted in the manuscript. The survey concludes the recent datasets considered, their key contributions and the performance of deep learning model applied with its primary purpose like sarcasm detection in order to describe the efficiency of deep learning frameworks in the domain of sentimental analysis.

scholarly journals Comparative Analysis of Deep Learning Techniques for the Classification of Hate Speech

2021 ◽  
Vol 4 (1) ◽  
pp. 121-128
Author(s):  
A Iorliam ◽  
S Agber ◽  
MP Dzungwe ◽  
DK Kwaghtyo ◽  
S Bum
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Or Groups ◽  
Long Short Term Memory

Social media provides opportunities for individuals to anonymously communicate and express hateful feelings and opinions at the comfort of their rooms. This anonymity has become a shield for many individuals or groups who use social media to express deep hatred for other individuals or groups, tribes or race, religion, gender, as well as belief systems. In this study, a comparative analysis is performed using Long Short-Term Memory and Convolutional Neural Network deep learning techniques for Hate Speech classification. This analysis demonstrates that the Long Short-Term Memory classifier achieved an accuracy of 92.47%, while the Convolutional Neural Network classifier achieved an accuracy of 92.74%. These results showed that deep learning techniques can effectively classify hate speech from normal speech.

scholarly journals Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

2021 ◽  
Vol 40 ◽  
pp. 03030
Author(s):  
Mehdi Surani ◽  
Ramchandra Mangrulkar
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Freedom Of Speech ◽  
Learning Approaches ◽  
Learning Models ◽  
Media Usage ◽  
The Past ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Machine Learning Models

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

Deep Learning Techniques for Diabetic Retinopathy Detection

2020 ◽  
Vol 16 ◽  
Author(s):  
Sehrish Qummar ◽  
Fiaz Gul Khan ◽  
Sajid Shah ◽  
Ahmad Khan ◽  
Ahmad Din ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Blood Sugar ◽  
Automatic Detection ◽  
Multiclass Classification ◽  
The Body ◽  
Future Directions ◽  
Learning Techniques

Diabetes occurs due to the excess of glucose in the blood that may affect many organs of the body. The increase in blood sugar in the body causes many problems. One of the most prominent of these problems is Diabetic Retinopathy (DR). DR occurs due to the mutilation of the blood vessels in a retina. The detection of DR is complicated and time-consuming due to its features for the ophthalmologists. Therefore, automatic detection is required, recently different machine and deep learning techniques are being applied to detect and classify DR. In this paper, we conducted a study of the various techniques available in the literature for the identification/classification of DR, the datasets used, strengths and weaknesses of each method and provides the future directions. Moreover, we also discussed the different steps for the detection that are segmentation of blood vessels in a retina, detecting lesions and other abnormalities of DR in binary and multiclass classification.

scholarly journals Deep Learning for emotion analysis in Arabic tweets

2021 ◽  
Author(s):  
ENAS ABDEL HAKIM KHALIL ◽  
Enas .M.F. El Houby ◽  
Hoda .k. Mohamed
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Short Term Memory ◽  
Model Performance ◽  
Support Vector ◽  
Emotion Classification ◽  
Network Layers ◽  
Learning Techniques ◽  
Comparable Performance ◽  
Fully Connected

Abstract Expressing our emotions using text and emojis expressions became widespread through social media such as Facebook, Instagram, Twitter, Weibo, and LinkedIn. Nowadays, both organizations and individuals are interested in using social media to analyze people's opinions and extract sentiments and emotions. We proposed a model for multilabel emotion classification, using a bidirectional Long Short-term Memory BiLSTM deep network. It is evaluated on the Arabic tweets' dataset provided by SemEval 2018 for the E-c task. Several preprocessing steps, including ARLSTEM with some modifications, replacing emojis with corresponding text meaning from a manually built lexicon, and feature vector representation using Aravec word embedding is applied. The novelty in our research that it examines the effect of hyperparameter tuning on model performance, and it uses BiLSTM in all of its deep neural network layers. The proposed model achieves a comparable performance with state-of-the-art models using different machine learning and deep learning techniques. The system achieves about 9% enhancement in validation accuracy compared with the last best model in the same task using Support Vector classifier SVC; it outperforms the other deep neural networks (UNCCTeam) based on fully connected layers in micro F1 metric of about 4.4%.

scholarly journals COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

2022 ◽  
Vol 9 ◽  
Author(s):  
Zunera Jalil ◽  
Ahmed Abbasi ◽  
Abdul Rehman Javed ◽  
Muhammad Badruddin Khan ◽  
Mozaherul Hoque Abul Hasanat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Daily Lives ◽  
The Public ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Twitter Users

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

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