Detecting Compromised Social Network Accounts Using Deep Learning for Behavior and Text Analyses

2021 ◽  
Vol 11 (2) ◽  
pp. 97-109
Author(s):  
Steven Yen ◽  
Melody Moh ◽  
Teng-Sheng Moh
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Social Network ◽  
Personal Data ◽  
Multi Layer Perceptron ◽  
Online Identity ◽  
The World ◽  
Textual Content ◽  
Behavioral Information ◽  
Over Time

Social networks allow people to connect to one another. Over time, these accounts become an essential part of one's online identity. The account stores various personal data and contains one's network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Quantifying Virality of Information in Online Social Networks

2012 ◽  
Vol 4 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Abhishek Vaish ◽  
Rajiv Krishna G. ◽  
Akshay Saxena ◽  
Dharmaprakash M. ◽  
Utkarsh Goel
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Critical Factors ◽  
Asset Valuation ◽  
Valuation Technique ◽  
Information Item ◽  
Viral Nature ◽  
Over Time

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

Citizen-Government Collaborative Environment Using Social Networks

2019 ◽  
pp. 152-165
Author(s):  
Hany Abdelghaffar ◽  
Lobna Hassan
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Social Network ◽  
Local Governments ◽  
Local Level ◽  
Quantitative Approach ◽  
Collaborative Environment ◽  
Electronic Democracy ◽  
Democratic Change ◽  
The World

Electronic democracy is a concept which is used in some countries around the world with mixed success. Social networks helped in facilitating democracy and democratic change in several countries suggesting that they could be utilized as an e-democracy tool. This research proposed a new model of how the decision-making process for local governments could be improved via social networks. Quantitative approach was used to investigate how the use of a social network amongst people living in the same suburb could improve decision making on the local level. Findings showed that awareness building, deliberation, and consultation factors could be used to affect the decision making for their local governments.

scholarly journals Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kefei Cheng ◽  
Xiaoyong Guo ◽  
Xiaotong Cui ◽  
Fengchi Shan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Social Network ◽  
Information Diffusion ◽  
Recommendation Algorithm ◽  
Central Sequence ◽  
Modeling Analysis ◽  
News Recommendation ◽  
Browsing Behavior ◽  
And Control

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

scholarly journals Frailty and Social Isolation: Comparing the Relationship between Frailty and Unidimensional and Multifactorial Models of Social Isolation

2020 ◽  
Vol 32 (10) ◽  
pp. 1297-1308
Author(s):  
John Maltby ◽  
Sarah A. Hunt ◽  
Asako Ohinata ◽  
Emma Palmer ◽  
Simon Conroy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Isolation ◽  
Nuclear Family ◽  
Frailty Index ◽  
Clinical Syndrome ◽  
Reciprocal Relationship ◽  
The Relationship ◽  
Multidimensional Models ◽  
Over Time

Objective: The aim of the study was to compare uni- and multidimensional models of social isolation to improve the specificity of determining associations between social isolation and frailty. Methods: The study included participants aged ≥60 years from the English Longitudinal Study of Ageing assessed for social isolation and frailty (frailty index and Fried phenotype) over a 4-year period. Factor analysis assessed whether social isolation was multidimensional. Multiple regression analysis was used to assess specificity in associations between social isolation and frailty over time. Results: Social isolation comprises social isolation from nuclear family, other immediate family, and wider social networks. Over time, social isolation from a wider social network predicted higher frailty index levels, and higher frailty index and Fried phenotype levels predicted greater social isolation from a wider social network. Discussion: Social isolation is multidimensional. The reciprocal relationship between social isolation from wider social networks and accumulating frailty deficits, and frailty as a clinical syndrome influencing social isolation from social networks is discussed.

scholarly journals Silent Listeners: The Evolution of Privacy and Disclosure on Facebook

2013 ◽  
Vol 4 (2) ◽  
Author(s):  
Fred Stutzman ◽  
Ralph Gross ◽  
Alessandro Acquisti
Keyword(s):  
Social Network ◽  
Personal Information ◽  
Social Network Sites ◽  
Personal Data ◽  
Extended Period ◽  
Third Party ◽  
Privacy Concerns ◽  
Trade Offs ◽  
Seeking Behavior ◽  
Over Time

Over the past decade, social network sites have experienced dramatic growth in popularity, reaching most demographics and providing new opportunities for interaction and socialization. Through this growth, users have been challenged to manage novel privacy concerns and balance nuanced trade-offs between disclosing and withholding personal information. To date, however, no study has documented how privacy and disclosure evolved on social network sites over an extended period of time. In this manuscript we use profile data from a longitudinal panel of 5,076 Facebook users to understand how their privacy and disclosure behavior changed between 2005---the early days of the network---and 2011. Our analysis highlights three contrasting trends. First, over time Facebook users in our dataset exhibited increasingly privacy-seeking behavior, progressively decreasing the amount of personal data shared publicly with unconnected profiles in the same network. However, and second, changes implemented by Facebook near the end of the period of time under our observation arrested or in some cases inverted that trend. Third, the amount and scope of personal information that Facebook users revealed privately to other connected profiles actually increased over time---and because of that, so did disclosures to ``silent listeners'' on the network: Facebook itself, third-party apps, and (indirectly) advertisers. These findings highlight the tension between privacy choices as expressions of individual subjective preferences, and the role of the environment in shaping those choices.

scholarly journals Designing for deep learning in the context of digital and social media

Comunicar ◽  
2019 ◽  
Vol 27 (58) ◽  
pp. 9-18 ◽  
Author(s):  
James-Paul Gee ◽  
Moisés Esteban-Guitart
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
World Health ◽  
Human Beings ◽  
Redes Sociales ◽  
Living Things ◽  
The World ◽  
Mind And Body ◽  
Health Organization

There is today a great deal of controversy over digital and social media. Even leaders in the tech industry are beginning to decry the time young people spend on smartphones and social networks. Recently, the World Health Organization proposed adding “gaming disorder” to its official list of diseases, defining it as a pattern of gaming behavior so severe that it takes “precedence over other life interests”. At the same time, many others have celebrated the positive properties of video games, social media, and social networks. This paper argues that a deeper understanding of human beings is needed to design for deep learning. For the purposes of this study “design for deep learning” means helping people matter and find meaning in ways that make them and others healthy in mind and body, while improving the state of the world for all living things, with due respect for truth, sensation, happiness, imagination, individuality, diversity, and the future. In particular, fifteen features related to human nature are suggested based on recent scientific developments to answer the question: What is a human being? Consequently, proposals that are linked to learning and transformation, as well as social improvement, should fit with the ways in which humans, as specific sorts of biological and social creatures, learn best (or can learn at all) and can change for the better. En la actualidad existe una nutrida controversia en relación a los medios de comunicación sociales y digitales que ha llevado, incluso, a censurar la utilización de las redes sociales y los móviles por parte de líderes en la industria tecnológica. En este sentido, la Organización Mundial para la Salud ha propuesto añadir el «desorden del juego» a su listado de enfermedades, definiéndolo como un modelo de comportamiento de juego tan severo que se impone como «preferencia sobre otros intereses». Al mismo tiempo, distintos académicos han enfatizado los aspectos positivos derivados de las redes sociales y los videojuegos. En este artículo se argumenta que es necesaria una mejor comprensión del ser humano para poder implementar lo que aquí se define como diseño para el aprendizaje profundo. El «diseño para el aprendizaje profundo» está encaminado al reconocimiento de las personas y el desarrollo de sentidos saludables, individual y colectivamente, así como la mejora, en general, del estado del mundo para todos los seres vivos, según principios de verdad, felicidad, imaginación, individualidad, diversidad y futuro. En particular, se sugieren quince características basadas en desarrollos científicos que responden a la pregunta: ¿Qué es un ser humano? Consecuentemente, propuestas vinculadas al aprendizaje y la transformación y mejora social deben ser coherentes con dichas características que permiten definir cómo las personas, en tanto que organismos biológicos y sociales, aprenden o pueden aprender óptimamente, así como cambiar para mejorar.

scholarly journals About one of the modern trends of business digitalization

2021 ◽  
pp. 35-38
Author(s):  
Anna ZADOROZHNA
Keyword(s):  
Social Networks ◽  
Personal Data ◽  
Positive Trend ◽  
Remote Communication ◽  
Average Growth ◽  
World Average ◽  
Business Partners ◽  
Advantages And Disadvantages ◽  
The World ◽  
Internet Penetration

Introduction. Currently, a business both organizes its sales through social networks and solves various business problems for them, for example, finding employees and business partners, expanding sales markets. Companies that want to grow successfully, to be in demand in the market, must pay close attention to social networks. The purpose of the paper is to study the role of social networks in the functioning of business, establishing and maintaining business communications, and assessing the advantages and disadvantages of different types of business networks. Results. Internet penetration in Ukraine in January 2021 is higher than the world average – 67.6 % or 29.47 million users, and the average growth of Internet users in Ukraine – by 7.3 % or 2 million people. A positive trend in the use of social networks in Ukraine has been observed in recent years also. Thus, their use for business communications in Ukraine is a promising way to establish business contacts. Among the most popular social networks in Ukraine in recent years are Facebook, YouTube and Instagram. If the promotion of goods and services is appropriate through non-professional social networks, it is better to address business issues in professional networks, such as business social networks. Their purpose is to establish business contacts for job search, business partners, meet potential employers, announce vacancies and search for employees, investors, exchange experiences, etc. The disadvantages of using social networks for business are: the ability of attackers to access personal data, depersonalization of contacts etc. Business social networks also include corporate social networks. They help to organize and manage the staff distributed by the corporation's branches with the help of modern forms of business communication. Conclusion. Ukraine is characterized by a higher than the world average, Internet penetration. The same trend is observed in relation to the use of social networks in Ukraine. Although social networks for private use serve as a good platform for advertising and selling goods, from a business point of view, business social networks are the most appropriate for solving business issues and establishing business contacts. Their importance is growing due to the growing globalization of the economy, as well as the need for remote communication, which is due to the danger of the spread of Covid-19.

scholarly journals Election Control in Social Networks via Edge Addition or Removal

2020 ◽  
Vol 34 (02) ◽  
pp. 1878-1885
Author(s):  
Matteo Castiglioni ◽  
Diodato Ferraioli ◽  
Nicola Gatti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Result ◽  
Optimization Problem ◽  
Optimal Solution ◽  
The Social ◽  
Wide Range ◽  
Local Modification ◽  
Edge Addition ◽  
Over Time

We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time).

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