scholarly journals Detecting Bots and Assessing Their Impact in Social Networks

2021 ◽  
Author(s):  
Nicolas Guenon des Mesnards ◽  
David Scott Hunter ◽  
Zakaria el Hjouji ◽  
Tauhid Zaman
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Statistical Physics ◽  
Opinion Dynamics ◽  
Data Sets ◽  
Social Network Data ◽  
Operational Capabilities ◽  
Dynamics Models ◽  
The Impact ◽  
The U.S

Bots Impact Opinions in Social Networks: Let’s Measure How Much There is a serious threat posed by bots that try to manipulate opinions in social networks. In “Assessing the Impact of Bots on Social Networks,” Nicolas Guenon des Mesnards, David Scott Hunter, Zakaria el Hjouiji, and Tauhid Zaman present a new set of operational capabilities to detect these bots and measure their impact. They developed an algorithm based on the Ising model from statistical physics to find coordinating gangs of bots in social networks. They then created an algorithm based on opinion dynamics models to quantify the impact that bots have on opinions in a social network. They applied their algorithms to a variety of real social network data sets. They found that, for topics such as Brexit, the bots had little impact, whereas for topics such as the U.S. presidential debate and the Gilets Jaunes protests in France, the bots had a significant impact.

scholarly journals Effectiveness of the Execution and Prevention of Metric-Based Adversarial Attacks on Social Network Data †

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 306
Author(s):  
Nikolaus Nova Parulian ◽  
Tiffany Lu ◽  
Shubhanshu Mishra ◽  
Mihai Avram ◽  
Jana Diesner
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Social Systems ◽  
Target Function ◽  
Centrality Measures ◽  
Network Data ◽  
Social Network Data ◽  
Empirical Networks ◽  
The Impact

Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality measures. This paper brings insights from research on adversarial attacks on machine learning systems to the domain of social networks by studying strategies by which an adversary can minimally perturb the observed network structure to achieve their target function of modifying the ranking of a target node according to centrality measures. This can represent the attempt of an adversary to boost or demote the degree to which others perceive individual nodes as influential or powerful. We study the impact of adversarial attacks on targets and victims, and identify metric-based security strategies to mitigate such attacks. We conduct a series of controlled experiments on synthetic network data to identify attacks that allow the adversary to achieve their objective with a single move. We then replicate the experiments with empirical network data. We run our experiments on common network topologies and use common centrality measures. We identify a small set of moves that result in the adversary achieving their objective. This set is smaller for decreasing centrality measures than for increasing them. For both synthetic and empirical networks, we observe that larger networks are less prone to adversarial attacks than smaller ones. Adversarial moves have a higher impact on cellular and small-world networks, while random and scale-free networks are harder to perturb. Also, empirical networks are harder to attack than synthetic networks. Using correlation analysis on our experimental results, we identify how combining measures with low correlation can aid in reducing the effectiveness of adversarial moves. Our results also advance the knowledge about the robustness of centrality measures to network perturbations. The notion of changing social network data to yield adversarial outcomes has practical implications, e.g., for information diffusion on social media, influence and power dynamics in social systems, and developing solutions to improving network security.

scholarly journals Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

2021 ◽  
Vol 11 (11) ◽  
pp. 4768
Author(s):  
Sanaa Kaddoura ◽  
Maher Itani ◽  
Chris Roast
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Arabic Language ◽  
Network Data ◽  
Arabic Text ◽  
Social Network Data ◽  
Dialectal Arabic ◽  
The Impact ◽  
Modern Standard

With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

scholarly journals Personality and the Impact of Celebrity Endorsements on Instagram

2021 ◽  
Vol 18 (3) ◽  
pp. 468-474
Author(s):  
Yair Amichai-Hamburger ◽  
Shir Etgar ◽  
Hadar Gil-Ad ◽  
Michal Levitan-Giat ◽  
Gaya Raz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Locus Of Control ◽  
Strong Influence ◽  
Online Social Network ◽  
Strong Impact ◽  
Celebrity Endorsements ◽  
Internal Locus Of Control ◽  
The Impact ◽  
The Relationship

Celebrities are famous people who often belong to entertainment industry. They are known to have a strong influence on people’s behavior. In the digital age this impact has expanded to include the online arena. Celebrities increasingly utilize Instagram, an online social network, to promote commercial products. It is important to learn to what extent people are influenced by this type of promotion and what sort of people are likely to be swayed by it. Research has demonstrated that people’s personalities have a strong impact on their behaviors online. However, until now, these investigations have not included the relationship between personality and the degree of celebrity influence through social networks. This study examines how much the personality of a user is related to the degree to which he or she is influenced by these Celebrity Instagram messages. Participants comprised 121 students (34 males, 87 females). They answered questionnaires which focused on their personality and were asked about the degree of influence celebrities exerted upon them through Instagram. Results showed that people who are characterized as being open and having an internal locus of control are more resistant to such celebrity influences. This paper demonstrates that the personality of a recipient is likely to influence the degree of impact that a celebrity endorsement is likely to produce. The implications of these results are discussed.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals LIFE ON DISPLAY: WHY IT IS IMPORTANT TO EXPOSE YOUR ACTIONS ON THE SOCIAL NETWORK

2021 ◽  
Vol 6(72) (4) ◽  
pp. 41-48
Author(s):  
A. E. Starchenko ◽  
M. V. Semina
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Social Network ◽  
Reference Group ◽  
Human Life ◽  
Sociological Study ◽  
The Social ◽  
Personal Accounts ◽  
The Impact ◽  
To Receive

Social networks have emerged relatively recently in human life, but have already become an integral part of it. Companies tell about themselves, their activities, innovations, promotions and events in their profiles. This helps increase audience coverage, tell more about your brand, products, services. People in personal accounts have the opportunity to share their lives and creativity through photos, videos and texts. Now it is not necessary to receive higher education to become an operator, director or actor whose talent is recognized by society. It is enough to start a page on the social network and start sharing your knowledge and creativity. To find out why people post photos, videos and write texts on their social networks, a pilot sociological study was carried out. The method of deep interview with active users of social networks was chosen to carry out the study. The interview allowed getting unique information, to learn the opinion of users about social networks, the impact of the new way of communication on their life, to identify the reasons why users start and maintain profiles. The respondents were 20 users of social networks between the ages of 19 and 22. Interviewees have profiles on the most popular Instagram and Vkontakte networks. As a result of the analysis of the interview, a tendency was revealed to differ in the perception of users of their actions on the social network and similar actions of other users. Their content is perceived by them as opportunities to be in sight, as a resource to form their social status and an element of influence on their reference group. And the same content published by others is perceived as boasting.

Multi-state voter model on weighted social networks with committed agents

2014 ◽  
Vol 25 (07) ◽  
pp. 1450022 ◽  
Author(s):  
Saijun Chen ◽  
Haibo Hu ◽  
Jun Chen ◽  
Zhigao Chen
Keyword(s):  
Social Networks ◽  
Numerical Simulations ◽  
Opinion Dynamics ◽  
The Other ◽  
Voter Model ◽  
Scaling Exponent ◽  
Dynamics Model ◽  
Topological Structures ◽  
A Value ◽  
Dynamics Models

There exist scaling correlations between the edge weights and the nodes' degrees in weighted social networks. Based on the empirical findings, we study a multi-state voter model on weighted social networks where the weight is given by the product of agents' degrees raised to a power θ and there exist persistent individuals whose opinions are independent of those of their friends. We find that the fraction of each opinion will converge to a value which only relates to the degrees of initial committed agents and the scaling exponent θ. The analytical predictions are verified by numerical simulations. The model indicates that agents' degrees and scaling exponent can significantly influence the final coexistence or consensus state of opinions. We also study the influence of degree mixing characteristics on the dynamics model by numerical simulations and discuss the relation between the model and the other related opinion dynamics models on social networks with different topological structures and initial configurations.

The Impact of Social Networks on Science Students' Academic Performance in Ondo State Senior Secondary Schools, Nigeria

2021 ◽  
pp. 50-63
Author(s):  
Jethro Oludare OLOJO
Keyword(s):  
Social Networks ◽  
Academic Performance ◽  
Social Network ◽  
Science Students ◽  
The Social ◽  
Ondo State ◽  
Research Questions ◽  
Senior Secondary Schools ◽  
Senior Secondary ◽  
The Impact

The objective of this study was to examine the impact of social network usage on science students’ academic achievements in Ondo State’s senior secondary schools. The study was also to find the extent to which students under investigation used the social network platforms and the frequencies of their visits. In order to achieve this, a structured questionnaire was designed and administered to students from the three senatorial districts that made up the state. A multistage; which involved simple random and purposive sampling approaches was used to select the sample for the study. 150 copies of the questionnaire were distributed; out of which, 148 (98.78%) copies were returned. For the study, four research questions and two research hypotheses were developed. The hypotheses were assessed using the student's - t statistic at 0.05 significant level; using SPSS version 20 while the research questions formulated were evaluated using frequency counts and percentages. The study revealed that Ondo State senior secondary school science students can efficiently use the social network platforms for academic activities with male students being more proficient than their female counterparts. The study also revealed that the usage of social networks has assisted students to improve their academic performance; irrespective of their classes. Besides, the study showed that Facebook was the most popular of all the social network platforms. To this end, the researcher recommended that teachers, parents, and guidance should monitor the activities of their wards on the social network sites so that they can use the platforms to benefit their lots. Teachers should also use the advantage of students’ exposure to social networking to change their teaching methods from traditional one to online teaching.

The Impact of Search Engines on Virus Propagation

2021 ◽  
pp. 2150230
Author(s):  
Cai Fu ◽  
Zhaokang Ke ◽  
Yunhe Zhang ◽  
Xiwu Chen ◽  
Liqing Cao ◽  
...  
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Data Sets ◽  
Immune Mechanism ◽  
Virus Propagation ◽  
Immunization Strategy ◽  
Immune Mechanisms ◽  
Social Network Data ◽  
Information Engineering ◽  
The Impact

With the popularization of computers and the development of information engineering, the emergence of search engines makes it possible to get the information needed from big data quickly and efficiently. However, in recent years, a multiplicity of new viruses have been propagated by search engines. Many researchers choose to cut off the source of virus propagation, ignoring the virus immunization strategy based on the search engine. In this paper, we analyze the impact of search engines on virus propagation. First, considering the immune effect and cost, two kinds of immune mechanisms based on the search engine that have greater practicability are defined. Second, immune mechanisms based on the search engine are theoretically analyzed by the iteration method and the dynamic method. The results show that this immunization strategy can slow down or eliminate the propagation of a virus to a certain extent. Third, three real social network data sets are used to simulate and analyze the immune mechanism. We find that when the proportion of nodes being infected and the proportion of infected nodes being identified by the search engine satisfy a certain relationship, our immune mechanism can inhibit the spread of viruses, which confirms our theoretical analysis results.

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