scholarly journals Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

2021 ◽  
Vol 71 ◽  
pp. 1049-1090
Author(s):  
Matteo Castiglioni ◽  
Diodato Ferraioli ◽  
Nicola Gatti ◽  
Giulia Landriani
Keyword(s):  
Social Network ◽  
Social Influence ◽  
Optimal Solution ◽  
Worst Case ◽  
Edge Removal ◽  
Independent Cascade Model ◽  
The Social ◽  
Special Cases ◽  
Wide Range ◽  
Local Modification

We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent  by seeds and spreading through the network according to the independent cascade model.  We provide a comprehensive theoretical study of the election control problem, investigating  two forms of manipulations: seeding to buy influencers given a social network and removing  or adding edges in the social network given the set of the seeds and the information sent.  In particular, we study a wide range of cases distinguishing in the number of candidates or  the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in  the worst-case, even when one accepts to get an approximation of the optimal margin of  victory, except for the case of seeding when the number of hard-to-manipulate voters is not  too large, and the number of uncertain voters is not too small, where we say that a voter  that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way  to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases.  More precisely, in the case of seeding, we show that the manipulation is hard even if the  graph is a line and that a large class of algorithms, including most of the approaches  recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such  as undirected graphs with every node having a degree at most two or directed trees. In the  case of edge removal or addition, our hardness results also apply to election manipulation  when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary  number of edges, and to the basic case of social influence maximization/minimization in  the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold  in a re-optimization variant, where the manipulator already knows an optimal solution  to the problem and computes a new solution once a local modification occurs, e.g., the  removal/addition of a single edge.

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).

The Social Network, Support and Neurosis

1977 ◽  
Vol 131 (2) ◽  
pp. 185-191 ◽  
Author(s):  
Scott Henderson
Keyword(s):  
Social Network ◽  
Primary Group ◽  
Positive Interaction ◽  
Network Support ◽  
The Social ◽  
Wide Range ◽  
Medical Consultations ◽  
Social Network Support ◽  
Habitual Responses ◽  
Medical Morbidity

SummaryThe psychological function of the social network is considered in terms of attachment theory. Social bonds are proposed as essential for obtaining a commodity commonly but unsatisfactorily referred to as support. Requirements for this complex commodity can be discerned in a wide range of contexts. Examples considered are the evolutionary origin of the social network itself, the concept of psychosocial supplies, the distribution of neurosis in Western and non-Western populations, the use of medical consultations, psychotherapy and habitual responses to adversity or disaster. In these and other contexts, it is apparent that individuals have, quite simply, a requirement for affectively positive interaction with others. Under stressful conditions this interaction is called ‘support’. When support is lacking there is evidence that psychiatric and perhaps medical morbidity rates increase. For research, the objective must now be to determine whether depleted primary group interaction is causally related to morbidity, or whether it is only an associated or a secondary factor in aetiology, or indeed wholly unrelated. Elucidating more precisely why people need people constitutes an important new task for social psychiatry.‘Thank you for your support; I shall wear it at all times.’Neddy Seagoon inThe Goon Show(Spike Milligan, 1959)

scholarly journals Why and When "Flawed" Social Network Analyses Still Yield Valid Tests of no Contagion

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Tyler J. VanderWeele ◽  
Elizabeth L. Ogburn ◽  
Eric J. Tchetgen Tchetgen
Keyword(s):  
Social Network ◽  
Social Influence ◽  
Confidence Intervals ◽  
Social Contagion ◽  
Statistical Dependence ◽  
Network Analyses ◽  
The Social ◽  
Social Network Analyses

Lyons (2011) offered several critiques of the social network analyses of Christakis and Fowler, including issues of confounding, model inconsistency, and statistical dependence in networks. Here we show that in some settings, social network analyses of the type employed by Christakis and Fowler will still yield valid tests of the null of no social contagion, even though estimates and confidence intervals may not be valid. In particular, we show that if the alter's state is lagged by an additional period, then under the null of no contagion, the problems of model inconsistency and statistical dependence effectively disappear which allow for testing for contagion. Our results clarify the setting in which even "flawed" social network analyses are still useful for assessing social contagion and social influence.

scholarly journals The Impact of Community Structure on the Convergence Time of Opinion Dynamics

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
An Lu ◽  
Chunhua Sun ◽  
Yezheng Liu
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Social Influence ◽  
Opinion Dynamics ◽  
Matrix Analysis ◽  
Convergence Time ◽  
Influence Matrix ◽  
The Social ◽  
The Impact ◽  
Second Largest Eigenvalue

We analyze the convergence time of opinion dynamics in a social network with community structure. Using matrix analysis, we prove that the convergence time is determined by the second largest eigenvalue modulus. This modulus is close to 1 if the social influence matrix is nearly uncoupled. Furthermore, we discuss and analyze the factors of community structure affecting the convergence time.

Scholarly Influence Research (SIR)

2012 ◽  
Vol 2 (1) ◽  
pp. 17-31 ◽  
Author(s):  
Hirotoshi Takeda ◽  
Duane P. Truex ◽  
Michael J. Cuellar ◽  
Richard Vidgen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Influence ◽  
The Social ◽  
Scholarly Influence ◽  
Research Findings ◽  
Publication Counts ◽  
Definition Of ◽  
Ideational Influence

Following previous research findings, this paper argues that the currently predominant method of evaluating scholar performance - publication counts in “quality” journals - is flawed due to the subjectivity inherent in the generation of the list of approved journals and absence of a definition of quality. Truex, Cuellar, and Takeda (2009) improved on this method by substituting a measurement of “influence” using the Hirsch statistics to measure ideational influence. Since the h-family statistics are a measure of productivity and the uptake of a scholar’s ideas expressed in publications, this methodology privileges the uptake of a scholar’s ideas over the venue of publication. Influence is built through other means than by having one’s papers read and cited. The interaction between scholars resulting in co-authored papers is another way to build scholarly influence. This aspect of scholarly influence, which the authors term social influence, can be assessed by Social Network Analysis (SNA) metrics that examine the nature and strength of coauthoring networks among IS Scholars. The paper demonstrates the method of assessing social influence by analysis of the social network of AMCIS scholars and compares the results of this analysis with other co-authorship networks from the ECIS and ICIS communities.

scholarly journals An efficient hybrid system for anomaly detection in social networks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Md. Shafiur Rahman ◽  
Sajal Halder ◽  
Md. Ashraf Uddin ◽  
Uzzal Kumar Acharjee
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Security And Privacy ◽  
Support Vector ◽  
The Social ◽  
Wide Range

AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

scholarly journals Addressing problematic consumption: an action research experience from the Social Network Analysis

AWARI ◽  
2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Juan José Vera ◽  
Nicolás Barroso
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Treatment ◽  
Social Intervention ◽  
Social Suffering ◽  
Research Experience ◽  
Treatment Groups ◽  
The Social ◽  
Wide Range

This paper is classified under the trend of studies that make use of ‘Social Network Analysis’ (SNA) to serve as guidelines for social intervention. Within the field of SNA, work has been carried out based on what is referred to as the socio-centric approach, with the aim of revealing a type of complete network, the Subjective Communities Networks, which are built from Community Treatment Groups pertaining to the Argentine Office of Drug-related Comprehensive Policies (SEDRONAR for its Spanish acronym) in order to address problematic abuse in socially vulnerable backgrounds. These groups belong to the ECO2 model, which was devised to intervene in a wide range of social suffering phenomena, and uses the SNA as a theoretical and methodological viewpoint for assessing people and communities. This thought is an attempt to answer the following question: how does SNA help formulate social intervention strategies for SEDRONAR groups in the Province of Mendoza?

scholarly journals Social Network Analysis in Russian Literary Studies

2020 ◽  
pp. 517-536
Author(s):  
Frank Fischer ◽  
Daniil Skorinkin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Literary Texts ◽  
Literary Studies ◽  
War And Peace ◽  
The Social ◽  
Wide Range ◽  
Russian Literary ◽  
Simple Fashion

AbstractNetwork analysis as a method has applications in a wide range of fields from physics to epidemiology and from sociology to political science, and in the meantime has also reached the literary studies. Networks can be leveraged to examine intertextual relations or even artistic influences, but the main application so far has been the analysis of social formations and character interactions within fictional worlds. To make this possible, texts have to be formalized into a set of nodes and edges, where nodes represent characters and edges describe the relations between these characters in a very simple fashion: Do they or don’t they interact? Based on a selection of Russian plays and Tolstoy’s novel War and Peace, we will describe approaches to the social network analysis of literary texts.

scholarly journals WhatsApp Web for academic purposes in times of covid-19

Apertura ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 54-69
Author(s):  
Nataly Andrea Guiñez-Cabrera ◽  
◽  
Katherine Mansilla-Obando ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Influence ◽  
Qualitative Methodology ◽  
Unified Theory ◽  
Communication Tools ◽  
Use Of Technology ◽  
The Social ◽  
Facilitating Conditions ◽  
Educational Field

One of the most used communication tools is WhatsApp, which increased its use due to covid-19, along with other social networks. In the educational field, students are also increasingly adopting this application for academic purposes from their computers called WhatsApp Web. However, more knowledge is needed about the factors that influence the acceptance and use of this social network. Therefore, the purpose of this study is to understand from the perspective of students the factors of acceptance and use of WhatsApp Web for academic purposes during the covid-19 pandemic. A qualitative methodology was used to achieve this objective, through fourteen semistructural interviews with students from various disciplines and universities. The findings of this study were analyzed with the unified theory of acceptance and use of technology (UTAUT). Where a fifth factor teamworkwas incorporated, being additional to the factors already existing in this theory (the expectation of performance, the expectation of effort, the social influence and the facilitating conditions). This study provides new insights as it is a pioneering research that UTAUT uses to interpret the acceptance and use of WhatsApp Web for academic purposes.

scholarly journals Football Team Social Structure and Perceived Support for Reporting Concussion Symptoms: Insights from a Social Network Analysis

2019 ◽  
Vol 24 (6) ◽  
pp. 256-262
Author(s):  
Heidi A. Wayment ◽  
Ann H. Huffman ◽  
Monica Lininger ◽  
Patrick C. Doyle
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Influence ◽  
Perceived Support ◽  
Social Connections ◽  
Eigenvector Centrality ◽  
Football Team ◽  
The Social ◽  
Important Others

Social network analysis (SNA) is a uniquely situated methodology to examine the social connections between players on a team, and how team structure may be related to self-reported team cohesion and perceived support for reporting concussion symptoms. Team belonging was positively associated with number of friendship ties (degree; r = .23, p < .05), intermediate ties between teammates (betweenness; r = .21, p < .05), and support from both teammates (r = .21, p < .05) and important others (r = .21, p < .05) for reporting concussion symptoms. Additionally, an SNA-derived measure of social influence, eigenvector centrality, was associated with football identity (r = .34, p < .01), and less support from important others (r = –.24, p < .05) regarding symptom reporting. Discussion focuses on why consideration of social influence dynamics may help improve concussion-related education efforts.

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