Online Behavior Balancing Model for Influence Maximization in Twitter

2020 ◽  
Vol 13 ◽  
Author(s):  
Sakshi Agarwal ◽  
Shikha Mehta
Keyword(s):  
Online Social Networks ◽  
Viral Marketing ◽  
Online Behavior ◽  
Linear Threshold ◽  
Proposed Model ◽  
The Social ◽  
Independent Cascade ◽  
Network Platform ◽  
Improved Accuracy ◽  
Estimation Models

Background: Social influence estimation is an important aspect of viral marketing. The majority of the influence estimation models for online social networks are either based on Independent Cascade (IC) or Linear Threshold (LT) models. These models are based on some hypothesis: (1) process of influence is irreversible; (2) classification of user’s status is binary, i.e., either influenced or non-influenced; (3) process of influence is either single person’s dominance or collective dominance but not the both at the same time. However, these assumptions are not always valid in the real world, as human behavior is unpredictable. Objective: Develop a generalized model to handle the primary assumptions of the existing influence estimation models. Methods: This paper proposes a Behavior Balancing (BB) Model, which is a hybrid of IC and LT models and counters the underlying assumptions of the contemporary models. Results: The efficacy of the proposed model to deal with various scenarios is evaluated over six different twitter election integrity datasets. Results depict that BB model is able to handle the stochastic behavior of the user with up to 35% improved accuracy in influence estimation as compared to the contemporary counterparts. Conclusion: The BB model employs the activity or interaction information of the user over the social network platform in the estimation of diffusion and allows any user to alter their opinion at any time without compromising the accuracy of the predictions.

On the Aggression Diffusion Modeling and Minimization in Twitter

2022 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Marinos Poiitis ◽  
Athena Vakali ◽  
Nicolas Kourtellis
Keyword(s):  
Online Social Networks ◽  
Important Research ◽  
Diffusion Modeling ◽  
Propagation Modeling ◽  
Twitter Data ◽  
Edge Weighting ◽  
Linear Threshold ◽  
Important Research Topic ◽  
The Social ◽  
Independent Cascade

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

scholarly journals An information search model for online social Networks - MOBIRSE

2015 ◽  
Vol 35 (3) ◽  
pp. 76-83
Author(s):  
Miguel Angel Niño Zambrano ◽  
Iván Darío Cerón Moreno ◽  
Jhon Alberto Astaiza Perafán ◽  
Gustavo Adolfo Ramírez
Keyword(s):  
Social Networks ◽  
Information Search ◽  
Online Social Networks ◽  
Web Search ◽  
User Profile ◽  
Kappa Statistics ◽  
Internet Users ◽  
Proposed Model ◽  
The Social

Online Social Networks (OSNs) have been gaining great importance among Internet users in recent years.  These are sites where it is possible to meet people, publish, and share content in a way that is both easy and free of charge. As a result, the volume of information contained in these websites has grown exponentially, and web search has consequently become an important tool for users to easily find information relevant to their social networking objectives. Making use of ontologies and user profiles can make these searches more effective. This article presents a model for Information Retrieval in OSNs (MOBIRSE) based on user profile and ontologies which aims to improve the relevance of retrieved information on these websites. The social network Facebook was chosen for a case study and as the instance for the proposed model. The model was validated using measures such as At-k Precision and Kappa statistics, to assess its efficiency.

Bioluminescent proteins prediction with voting strategy.

2020 ◽  
Vol 15 ◽  
Author(s):  
Shulin Zhao ◽  
Ying Ju ◽  
Xiucai Ye ◽  
Jun Zhang ◽  
Shuguang Han
Keyword(s):  
Amino Acid ◽  
Model Building ◽  
Prediction Method ◽  
Pair Composition ◽  
Proposed Model ◽  
Counting Rules ◽  
Significant Phenomenon ◽  
Voting Strategy ◽  
Improved Accuracy ◽  
Selection Of

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology.In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this paper, we propose a new bioluminescent proteins prediction method based on a voting algorithm. We used four methods of feature extraction based on the amino acid sequence. We extracted 314 dimensional features in total from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, then used a voting algorithm to build the model.To create the best performing model, we discuss the selection of base classifiers and vote counting rules. Results: Our proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. We also improved a previous prediction of bioluminescent proteins in three lineages using our model building method, resulting in greatly improved accuracy.

scholarly journals Using a Quasi-Experimental Study to Examine Outcomes for Older Adult Intergenerational Program Participants

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 543-543
Author(s):  
Skye Leedahl ◽  
Melanie Brasher ◽  
Erica Estus
Keyword(s):  
Experimental Study ◽  
Rhode Island ◽  
Older Adult ◽  
Online Social Networks ◽  
Intergenerational Learning ◽  
Senior Center ◽  
Public Authorities ◽  
The Social ◽  
Quasi Experimental ◽  
The University

Abstract To more rigorously examine the University of Rhode Island Cyber-Seniors Program, we conducted a quasi-experimental study to examine if older adult senior center participants (n=25) improved scores on social and technological measures compared to a sample of senior center participants (n=25) who did not take part in the program. Findings showed that participants improved on technology measures compared to the non-participants, including searching and finding information about goods & services, obtaining information from public authorities or services, seeking health information, sending or receiving emails, and participating in online social networks (p<.05). However, participants did not change on social measures. There is either a need to identify better social measures to understand the social benefits of taking part, or to bolster the program to aid in helping older adults alleviate isolation and loneliness. Information on best practices and challenges for gathering outcomes from older participants will be discussed. Part of a symposium sponsored by Intergenerational Learning, Research, and Community Engagement Interest Group.

scholarly journals Big Data: Some Ethical Concerns for the Social Sciences

2021 ◽  
Vol 10 (2) ◽  
pp. 36
Author(s):  
Michael Weinhardt
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Ethical Issues ◽  
Online Behavior ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
And Behaviors ◽  
State Actors

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

scholarly journals Steganographic visual story with mutual-perceived joint attention

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yanyang Guo ◽  
Hanzhou Wu ◽  
Xinpeng Zhang
Keyword(s):  
Social Support ◽  
Social Media ◽  
Deep Learning ◽  
Joint Attention ◽  
Online Social Networks ◽  
Generation Model ◽  
High Quality ◽  
Visual Storytelling ◽  
The Social ◽  
User Intervention

AbstractSocial media plays an increasingly important role in providing information and social support to users. Due to the easy dissemination of content, as well as difficulty to track on the social network, we are motivated to study the way of concealing sensitive messages in this channel with high confidentiality. In this paper, we design a steganographic visual stories generation model that enables users to automatically post stego status on social media without any direct user intervention and use the mutual-perceived joint attention (MPJA) to maintain the imperceptibility of stego text. We demonstrate our approach on the visual storytelling (VIST) dataset and show that it yields high-quality steganographic texts. Since the proposed work realizes steganography by auto-generating visual story using deep learning, it enables us to move steganography to the real-world online social networks with intelligent steganographic bots.

scholarly journals Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1257 ◽  
Author(s):  
Liang Zhang ◽  
Quanshen Wei ◽  
Lei Zhang ◽  
Baojiao Wang ◽  
Wen-Hsien Ho
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relationships ◽  
Two Stage ◽  
Proposed Model ◽  
The Social ◽  
Item List ◽  
Ranking Strategy ◽  
High Prediction ◽  
Optimization Mechanism

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

scholarly journals Shared Partisanship, Household Norms and Turnout: Testing a Relational Theory of Electoral Participation

2016 ◽  
Vol 48 (3) ◽  
pp. 807-823 ◽  
Author(s):  
Edward Fieldhouse ◽  
David Cutts
Keyword(s):  
Party Identification ◽  
Household Survey ◽  
Relational Theory ◽  
Civic Duty ◽  
Proposed Model ◽  
The Social ◽  
Empirical Tests ◽  
Partisan Identification ◽  
Consumption Benefits ◽  
The Individual

Previous research shows that the household context is a crucial source of influence on turnout. This article sets out a relational theory of voting in which turnout is dependent on the existence of relational selective consumption benefits. The study provides empirical tests of key elements of the proposed model using household survey data from Great Britain. First, building on expressive theories of voting, it examines the extent to which shared partisan identification enhances turnout. Secondly, extending theories of voting as a social norm, it tests whether the civic norms of citizens’ families or households affect turnout over and above the social norms of the individual. In accordance with expectations of expressive theories of voting, it finds that having a shared party identification with other members of the household increases turnout. It also finds that the civic duty of other household members is important in explaining turnout, even when allowing for respondent’s civic duty.

Neighborhood Disorder, Urban Stressors, and Street Codes: A Model for Exploring Social Determinants of Life-Course Trajectories

2017 ◽  
Vol 1 (2) ◽  
pp. 89-103 ◽  
Author(s):  
M. Daniel Bennett
Keyword(s):  
Life Course ◽  
Social Determinants ◽  
Relevant Literature ◽  
African American Youth ◽  
Developmental Outcomes ◽  
Neighborhood Disorder ◽  
Proposed Model ◽  
The Social ◽  
Youth And Young Adults ◽  
Health Model

This article reviews relevant literature and proposes a theoretically grounded conceptual model by which to inform, and potentially advance, the exploratory study of the effects of neighborhood disorder on the psychosocial, emotional, and cultural pathways that are thought to influence social and developmental outcomes for African American youth and young adults. Similar to the social determinants of health model which asserts that the distribution of social and economic resources across populations influences differences in health status, the proposed model posits that environment determines social and developmental outcomes and hence life-course trajectories.

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