scholarly journals Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

2019 ◽  
Vol 20 (8) ◽  
pp. 3049-3058 ◽  
Author(s):  
Yuanyuan Chen ◽  
Yisheng Lv ◽  
Xiao Wang ◽  
Lingxi Li ◽  
Fei-Yue Wang
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Traffic Information ◽  
Learning Approaches ◽  
Media Texts

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).

scholarly journals Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5924
Author(s):  
Yi Ji Bae ◽  
Midan Shim ◽  
Won Hee Lee
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Mental Health Problems ◽  
Negative Emotion ◽  
Supervised Machine Learning ◽  
Control Group ◽  
Learning Approaches ◽  
Linguistic Features ◽  
Media Texts

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

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 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.

Abusive language detection from social media comments using conventional machine learning and deep learning approaches

2021 ◽  
Author(s):  
Muhammad Pervez Akhter ◽  
Zheng Jiangbin ◽  
Irfan Raza Naqvi ◽  
Mohammed AbdelMajeed ◽  
Tehseen Zia
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Learning Approaches ◽  
Conventional Machine ◽  
Language Detection

scholarly journals Deep Learning approaches for Fashion Knowledge Extraction from Social Media: a review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Marco Mameli ◽  
Marina Paolanti ◽  
Rocco Pietrini ◽  
Giulia Pazzaglia ◽  
Emanuele Frontoni ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Knowledge Extraction ◽  
Learning Approaches

scholarly journals A Review on the Detection of Offensive Content in Social Media Platforms

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Solomon Akinboro ◽  
Oluwadamilola Adebusoye ◽  
Akintoye Onamade
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hybrid Methods ◽  
Future Research ◽  
Learning Approaches ◽  
Hybrid Approaches ◽  
Use Of Social Media

Offensive content refers to messages which are socially unacceptable including vulgar or derogatory messages. As the use of social media increases worldwide, social media administrators are faced with the challenges of tackling the inclusion of offensive content, to ensure clean and non-abusive or offensive conversations on the platforms they provide.  This work organizes and describes techniques used for the automated detection of offensive languages in social media content in recent times, providing a structured overview of previous approaches, including algorithms, methods and main features used.   Selection was from peer-reviewed articles on Google scholar. Search terms include: Profane words, natural language processing, multilingual context, hybrid methods for detecting profane words and deep learning approach for detecting profane words. Exclusions were made based on some criteria. Initial search returned 203 of which only 40 studies met the inclusion criteria; 6 were on natural language processing, 6 studies were on Deep learning approaches, 5 reports analysed hybrid approaches, multi-level classification/multi-lingual classification appear in 13 reports while 10 reports were on other related methods.The limitations of previous efforts to tackle the challenges with regards to the detection of offensive contents are highlighted to aid future research in this area.  Keywords— algorithm, offensive content, profane words, social media, texts

Deep Learning Approaches to Overcome Challenges in Forensics

2021 ◽  
pp. 81-92
Author(s):  
Kiruthigha M. ◽  
Senthil Velan S.
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Social Network ◽  
Social Networking ◽  
Learning Approaches ◽  
Network Forensics ◽  
Cyber Forensics ◽  
Speed Up ◽  
Online Sources ◽  
Gain Access

Cyber forensics deals with collecting, extracting, analysing, and finally reporting the evidence of a crime. Typically investigating a crime takes time. Involving deep learning methods in cyber forensics can speed up the investigation procedure. Deep learning incorporates areas like image classification, morphing, and behaviour analysis. Forensics happens where data is. People share their activities, pictures, videos, and locations visited on the readily available platform, social media. An abundance of information available on social networking platforms renders them a favourite of cybercriminals. Compromising a profile, a hacker can gain access, modify, and use its data for various activities. Unscrupulous activities on such platforms include stalking, bullying, defamation, circulation of illegal or pornographic material, etc. Social network forensics is more than the application of computer investigation and analysis techniques, such as collecting information from online sources. CNNs and autoencoders can learn and obtain features from an image.

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