Positive and negative behavioral analysis in social networks

Squicciarini, Anna; Rajtmajer, Sarah; Griffin, Christopher

doi:10.1002/widm.1203

Cited by 4 publications

(3 citation statements)

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“…The most frequently cited academic articles and research summaries tend to focus on consumption theories [1], Internet studies [2] and impression management [3]. Moreover, the numbers of published behavioural analyses [4] and social media storytelling papers [5] have been increasing for a decade.…”

Section: Introductionmentioning

confidence: 99%

Digital identity and the online self: Footprint strategies – An exploratory and comparative research study

Fehér

2019

Journal of Information Science

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Reflecting on the thousands of diverse research studies of social media representation and digital privacy, this article presents a comprehensive summary of online personal strategies. First, the evolution of academic concepts about digital identity and the online self is summarised. Then, the article investigates the key dynamics of personal strategies and control issues in detail with ideas, experiences, stories and metaphors taken from 60 qualitative interviews from Central and Eastern Europe and Southeast Asia. According to the key findings of this article, the universal patterns of online personal strategies follow mostly conscious decisions, resulting in users maintaining 70% control of their digital footprints. However, the remaining 30% of online activities are unconscious floating with digital dynamics and resulting in a wide range of non-expected consequences from identity theft to kidnapping. In summary, an intercultural and intergenerational model highlights the complexity and diversity of the studied field, providing a reference framework for future studies. The closing section presents a discussion of those findings of this study that are inconsistent with commonplace assumptions and conclusions present in the academic literature, promoting for study those subjects that still need to be extended or explored.

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Section: Introductionmentioning

confidence: 99%

Digital identity and the online self: Footprint strategies – An exploratory and comparative research study

Fehér

2019

Journal of Information Science

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“…The Social Network Analysis (SNA) is the branch of sociology, which studies the structure of the social network and relationship among the social system using mathematics to extract some interesting patterns out of the analysis [7]. Many researchers performed SNA on various social networking sites to identify the influential persons for target marketing, but failed to make 360-degree profiling of the users [8][9][10][11][12]. Hence, this work aims to mine the Twitter data to get 360-degree profiling of users, which may help the business organizations to identify the influential persons and increase the efficiency of marketing campaigns.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

2019

IJITEE

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This paper aims to analyse the online social network for reconnaissance of people for finding their potentiality. The work considers one of the famous social networking sites, Twitter, where people express their thoughts and ideas, through which the people in the site knowingly or unknowingly reveal the information about themselves such as personal interests, likes and dislikes. The Machine Learning technique facilitates the work to mine the tweet data of a person to get his/her 360-degree profiling. This profiling is helpful to identify the personality type of a person, which is essential for the Government to identify the people involved in spreading the fake news in Twitter.

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“…The high usage of social networks provides a good opportunity for social network analysis. There are many different research directions in social network analysis, including information diffusion [1][2][3], community detection [4][5][6][7], link prediction [8][9][10] and behaviour analysis [11,12].…”

Section: Introductionmentioning

confidence: 99%

Finding influential users for different time bounds in social networks using multi-objective optimization

Mohammadi

Saraee

2018

Swarm and Evolutionary Computation

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Online social networks play an important role in marketing services. Influence maximization is a major challenge given its goal to find the most influential users in a social network. Increasing the number of influenced users at the end of a diffusion process while decreasing the time of diffusion are two main objectives of the influence maximization problem. The goal of this paper is to find multiple sets of influential users such that each of them is the best set to spread influence for a specific time bound. Considering two adverse objectives, increasing influence and decreasing diffusion time, we employ the NSGA-II algorithm which is a powerful algorithm in multi-objective optimization to find different seed sets with high influence at different diffusion times. Since social networks are large, computing influence and diffusion time of all chromosomes in each iteration will be challenging and computationally expensive. Therefore, we propose two methods which can estimate the expected influence and diffusion time of a seed set in an efficient manner. Providing the set of all potentially optimal solutions, help a decision maker evaluate the tradeoffs between the two objectives, i.e., the number of influenced users and diffusion time. In addition, we develop an approach for selecting seed sets, which have optimal influence for specific time bounds, from the resulting Pareto front of the NSGA-II. Finally, we show that applying our algorithm to real social networks outperforms existing algorithms for influence maximization problem. The results show a good compromise between two objectives and the final seed sets result in high influence for different time bounds.

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Positive and negative behavioral analysis in social networks

Cited by 4 publications

References 92 publications

Digital identity and the online self: Footprint strategies – An exploratory and comparative research study

Digital identity and the online self: Footprint strategies – An exploratory and comparative research study

A Machine Learning-based Online Social Network Analysis for 360-degree User Profiling

Finding influential users for different time bounds in social networks using multi-objective optimization

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