Suspicious Tweet Identification Using Machine Learning Approaches for Improving Social Media Marketing Analysis

2022 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Thamaraiselvan Natarajan ◽  
Senthil Arasu Balasubramanian ◽  
Jonath BackiaSeelan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Media Marketing ◽  
Learning Approaches ◽  
Marketing Analysis

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 Examination of ‘Interests’ and ‘Activities’ of Social Network users

2019 ◽  
Vol 9 (2) ◽  
pp. 2688-2693
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
General Population ◽  
Social Media Marketing ◽  
Family Status ◽  
Learning Methods ◽  
Attribute Data ◽  
Machine Learning Methods ◽  
The Social

The present study relates to the analysis of attribute data related to users of the social network VK. The general population N = 52,614 users is the intersection of audiences from two communities for social media marketing. Based on the collected statistics on the “interests” attribute, one can compile a generalized portrait of an IT specialist and online marketer: this is a man aged about 30 years old, not married, or who defines his family status as “everything is complicated”. He speaks an average of two languages, works for an organization, or studies at a university. He has about 370 followers on VK. The result based on the data from the field 'activities' is very close to the data from the field 'interests', and gives a similar picture of the generalized portrait of a specialist. As part of the study, the authors have learned how to segment users into the users that identify themselves as „IT specialists or online marketers‟, and „other‟ users, using machine learning methods

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

2020 ◽  
Vol 34 (5) ◽  
pp. 826-844 ◽  
Author(s):  
Louis Tay ◽  
Sang Eun Woo ◽  
Louis Hickman ◽  
Rachel M. Saef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Text Mining ◽  
Personality Assessment ◽  
Ground Truth ◽  
Psychometric Validation ◽  
Learning Approaches ◽  
Text Data ◽  
Personality Psychology ◽  
Social Media Text

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology

scholarly journals Correction to: 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

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 A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

2021 ◽  
Vol 9 (10) ◽  
pp. 1410-1440
Author(s):  
T Heena Fayaz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Networks ◽  
Social Media ◽  
Future Research ◽  
Learning Approaches ◽  
Electoral Campaigns ◽  
Regression Methods ◽  
New Research

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

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