Machine Learning Techniques for Mining Location-Based Social Networks for Business Predictions

Author(s):  
Ola Al Sonosy ◽  
Sherine Rady ◽  
Nagwa Lotfy Badr ◽  
Mohammed Hashem
Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 64
Author(s):  
Panagiotis Kantartopoulos ◽  
Nikolaos Pitropakis ◽  
Alexios Mylonas ◽  
Nicolas Kylilis

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.


Author(s):  
Ekaterina Popova ◽  
Vladimir Spitsyn

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.


2019 ◽  
Vol 4 (2) ◽  
pp. 133-149
Author(s):  
Jordan Frith ◽  
Rowan Wilken

In their book, Location-Based Social Media: Space, Time and Identity, Leighton Evans and Michael Saker remark on the apparent ‘death’ of location-based social networks, suggesting that location-based social networks can now be understood as ‘a form of “zombie-media” that animates and haunts other media platforms’. In this article, we use this perspective as a point of departure for a social shaping of technology-informed analysis of two key geomedia platforms: Yelp and Foursquare. With Yelp approaching its 15th year of service and Foursquare approaching its 10th anniversary, this article provides a timely opportunity to (re-)examine the significance of Yelp and Foursquare and the many reconfigurations both firms have made to their services since their launch. These include, most recently, Yelp’s integration of artificial intelligence/machine learning techniques to parse, sift and order users’ posts and Foursquare’s development of its Pilgrim SDK (software design kit) to power the location services of other platforms, like Tinder and Snap. A social shaping-inflected approach is productive in this context in that it stresses how many of these developments and strategic reorientations are not just in response to shareholder and investor pressures, they are also fundamentally shaped by and made in response to the fluctuating demands of end-users within a complicated, competitive and continuously evolving geomedia ecosystem. Consequently, we draw from the work of Leah A Lievrouw to examine how dual tensions of contingency/determination shape how these applications are designed and used, and how both design and use continue to evolve in response to various external pressures.


2018 ◽  
Vol 7 (2.32) ◽  
pp. 473
Author(s):  
Dorababu Sudarsa ◽  
Siva Kumar.P ◽  
L Jagajeevan Rao

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. 


Author(s):  
Andrea Tundis ◽  
Leon Böck ◽  
Victoria Stanilescu ◽  
Max Mühlhäuser

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. .


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