scholarly journals Veracity Assessment of Multimedia Facebook Posts for Infodemic Symptom Detection using Bi-modal Unsupervised Machine Learning Approach

2021 ◽  
Vol 9 (12) ◽  
pp. 2234-2241
Author(s):  
Taiwo Olapeju Olaleye
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Experimental Result ◽  
The Arts ◽  
Machine Learning Approach ◽  
Network Operations ◽  
The Subject ◽  
Pass Through ◽  
Media Sentiment ◽  
Social Engagements

Abstract: Ascertaining the truthfulness and trustworthiness of information posted on social media has been challenging with the proliferation of unsubstantiated, misleading, and inciting news, with different intents by purveyors. Unlike the traditional media with some level of regulations, user-generated posts on social networks does not pass through censorships in order to establish the truism of news items hence the need to be cautious of posted information on the networks. The lingering issue of recent suspension of Twitter microblogging site by the Nigerian government and the consequent decision to regulate social network operations in the country similarly centers on the subject of social media dependability for legitimate social engagements by millions of savvy Nigerian users. Whereas existing models in literature have proposed state-of-the-arts, this study seeks to improve on obtainable studies with a bi-modal machine learning methodology that indicate symptoms of infodemic social media posts. Using a multimedia facebook corpus, an unsupervised natural language processor, Inception v3 model, coupled with a hierarchical clustering network, is deployed for the duo of image and text sentiment analytics. Experimental result uniquely identified infodemic tendencies in facebook text-corpus and efficiently differentiates image-corpus into respective clusters through the Euclidian distance metrics. The most infodemic post returned a -0.9719 compound score while the most positive post returns 0.9488. Veracity assessment of polarized opinions expressed in negative clusters reveals that provocative, derogatory, obnoxious, etc. indicate propensity for infodemic tendencies. Keywords: Fake news. Facebook. Social media. Sentiment Analysis. Infodemic

scholarly journals News Classification Using Machine Learning

2021 ◽  
Vol 9 (5) ◽  
pp. 23-27
Author(s):  
SHWETA MAHAJAN
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Performance Improvement ◽  
Vital Role ◽  
Learning Approach ◽  
Entertainment Education ◽  
Meaningful Information ◽  
Textual Data ◽  
Machine Learning Approach

There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

scholarly journals Reviewing Sentiment Analysis at the Shallow End

2020 ◽  
Vol 8 (4) ◽  
pp. 47-62
Author(s):  
Francisca Oladipo ◽  
Ogunsanya, F. B ◽  
Musa, A. E. ◽  
Ogbuju, E. E ◽  
Ariwa, E.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
Training Data ◽  
Data Set ◽  
The Social ◽  
Machine Learning Approach ◽  
Media Space ◽  
Social Media Platforms

The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.  

scholarly journals Machine Learning Approach for User Accounts Identification with Unwanted Information and data

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

scholarly journals Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Hendry Cipta Husada ◽  
Adi Suryaputra Paramita
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Learning Approach ◽  
Social Media Platform ◽  
Machine Learning Approach ◽  
Media Platform ◽  
Fold Cross Validation

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

scholarly journals Destination Image of DMO and UGC on Instagram: A Machine-Learning Approach

2022 ◽  
pp. 343-355
Author(s):  
Roman Egger ◽  
Oguzcan Gumus ◽  
Elza Kaiumova ◽  
Richard Mükisch ◽  
Veronika Surkic
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Destination Image ◽  
User Generated Content ◽  
Learning Approach ◽  
Projected Image ◽  
Machine Learning Approach ◽  
Destination Brand ◽  
Practical Implications ◽  
Insight Into

AbstractSocial media plays a key role in shaping the image of a destination. Although recent research has investigated factors influencing online users’ perception towards destination image, limited studies encompass and compare social media content shared by tourists and destination management organisations (DMOs) at the same time. This paper aims to determine whether the projected image of DMOs corresponds with the destination image perceived by tourists. By taking the Austrian Alpine resort Saalbach-Hinterglemm as a case, a netnographic approach was applied to analyse the visual and textual posts of DMO and user-generated content (UGC) on Instagram using machine learning. The findings reveal themes that are not covered in the posts published by marketers but do appear in UGC. This study adds to the existing literature by providing a deeper insight into destination image formation and uses a qualitative approach to assess destination brand image. It further highlights practical implications for the industry regarding DMOs’ social media marketing strategy.

scholarly journals Learning-Based Stock Market Trending Analysis by Incorporating Social Media Sentiment Analysis

2021 ◽  
Author(s):  
Zhaoxia Wang ◽  
Zhenda HU ◽  
Fang LI ◽  
Seng-Beng HO
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Prediction Performance ◽  
Support Vector ◽  
Stock Market Prediction ◽  
Stock Price Prediction ◽  
Extreme Gradient Boosting ◽  
Media Sentiment

Abstract Stock market trending analysis is one of the key research topics in financial analysis. Various theories once highlighted the non-viability of stock market prediction. With the advent of machine learning and Artificial Intelligence (AI), more and more efforts have been devoted to this research area, and predicting the stock market has been demonstrated to be possible. Learning-based methods have been popularly studied for stock price prediction. However, due to the dynamic nature of the stock market and its non-linearity, stock market prediction is still one of the most dificult tasks. With the rise of social networks, huge amount of data is being generated every day and there is a gaining in popularity of incorporating these data into prediction model in the effort to enhance the prediction performance. Therefore, this paper explores the possibilities of the viability of learning-based stock market trending prediction by incorporating social media sentiment analysis. Six machine learning methods including Multi-Layer Perception, Support Vector Machine, Naïve Bayes, Random Forest, Logistic Regression and Extreme Gradient Boosting are selected as the baseline model. The result indicates the possibilities of successful stock market trending prediction and the performance of different learning-based methods is discussed. It is discovered that the distribution of the value of stocks may affect the prediction performance of the methods involved. This research not only demonstrates the merits and weaknesses of different learning-based methods, but also points out that incorporating social opinion is a right direction for improving the performance of stock market trending prediction.

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