scholarly journals Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

2020 ◽  
Author(s):  
Andrea Tundis ◽  
Leon Böck ◽  
Victoria Stanilescu ◽  
Max Mühlhäuser
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Online Social Networks ◽  
Data Availability ◽  
Digital Tools ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Public Dataset

Online social networks (OSNs) represent powerful digital tools to communicate and quickly disseminate information in a non-official way. As they are freely accessible and easy to use, criminals abuse of them for achieving their purposes, for example, by spreading propaganda and radicalising people. Unfortunately, due to their vast usage, it is not always trivial to identify criminals using them unlawfully. Machine learning techniques have shown benefits in problem solving belonging to different application domains, when, due to the huge dimension in terms of data and variables to consider, it is not feasible their manual assessment. However, since the OSNs domain is relatively young, a variety of issues related to data availability makes it difficult to apply and immediately benefit from such techniques, in supporting the detection of criminals on OSNs. In this perspective, this paper wants to share the experience conducted in using a public dataset containing information related to criminals in order to both (i) extract specific features and to build a model for the detection of terrorists on Facebook social network, and (ii) to highlight the current limits. The research methodology as well as the gathered results are fully presented and then the data-related issues, emerged from this experience, are discussed. .

scholarly journals Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 64
Author(s):  
Panagiotis Kantartopoulos ◽  
Nikolaos Pitropakis ◽  
Alexios Mylonas ◽  
Nicolas Kylilis
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Test Phase ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Specific Subject ◽  
Learning Techniques ◽  
Twitter Account

Social media has become very popular and important in people’s lives, as personal ideas, beliefs and opinions are expressed and shared through them. Unfortunately, social networks, and specifically Twitter, suffer from massive existence and perpetual creation of fake users. Their goal is to deceive other users employing various methods, or even create a stream of fake news and opinions in order to influence an idea upon a specific subject, thus impairing the platform’s integrity. As such, machine learning techniques have been widely used in social networks to address this type of threat by automatically identifying fake accounts. Nonetheless, threat actors update their arsenal and launch a range of sophisticated attacks to undermine this detection procedure, either during the training or test phase, rendering machine learning algorithms vulnerable to adversarial attacks. Our work examines the propagation of adversarial attacks in machine learning based detection for fake Twitter accounts, which is based on AdaBoost. Moreover, we propose and evaluate the use of k-NN as a countermeasure to remedy the effects of the adversarial attacks that we have implemented.

scholarly journals Sentiment Analysis of Short Russian Texts Using BERT and Word2Vec Embeddings

2021 ◽  
Author(s):  
Ekaterina Popova ◽  
Vladimir Spitsyn
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Functional Method ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Word Embeddings ◽  
Learning Techniques

This article is devoted to modern approaches for sentiment analysis of short Russian texts from social networks using deep neural networks. Sentiment analysis is the process of detecting, extracting, and classifying opinions, sentiments, and attitudes concerning different topics expressed in texts. The importance of this topic is linked to the growth and popularity of social networks, online recommendation services, news portals, and blogs, all of which contain a significant number of people's opinions on a variety of topics. In this paper, we propose machine-learning techniques with BERT and Word2Vec embeddings for tweets sentiment analysis. Two approaches were explored: (a) a method, of word embeddings extraction and using the DNN classifier; (b) refinement of the pre-trained BERT model. As a result, the fine- tuning BERT outperformed the functional method to solving the problem.

Thwarting Spam on Facebook

2018 ◽  
pp. 51-70
Author(s):  
Arti Jain ◽  
Reetika Gairola ◽  
Shikha Jain ◽  
Anuja Arora
Keyword(s):  
Machine Learning ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Testing Dataset ◽  
Learning Techniques ◽  
Textual Image ◽  
Combined Feature ◽  
Entire Dataset ◽  
F Measure

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.

scholarly journals Sentiment Analysis for Social Networks Using Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 473
Author(s):  
Dorababu Sudarsa ◽  
Siva Kumar.P ◽  
L Jagajeevan Rao
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Vast Amount ◽  
Learning Techniques ◽  
Precision And Accuracy

The tremendous of the overall enormous net has conveyed a present day way of communicating the feelings of individuals. It's additionally a medium with a vast amount of data in which clients can see the assessment of different clients which can be ordered into exceptional entailment summons and are progressively more boom as a key component in decision making. This paper adds to the supposition assessment for customers assessment class that is utilized to analyze the records inside the type of the assortment of tweets wherein investigates are very unstructured and are both high fine or terrible, or somewhere in the middle of these . For this we first pre-prepared the dataset, after that extract the adjective from the dataset that has a couple of significance this is alluded to as capacity vector, at that point decided on the component vector posting and from that point accomplished device examining based write calculations particularly navie bayes, most entropy and svm along the edge of the semantic introduction based absolutely based on word net which extracts synonyms and similarity for the content characteristic. In the end, we measured the performance of the classifier in terms of considering, precision and accuracy. 

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