Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Data Collection
Studying Influentials Across Sites
Conclusions
References
All Topics
Computer Science
Artificial Intelligence

Analyzing Behavior of the Influentials Across Social Media

Profile image of Shamanth KumarShamanth Kumar

2012, Behavior Computing

https://doi.org/10.1007/978-1-4471-2969-1_1
visibility

…

description

16 pages

link

1 file

Abstract
sparkles

AI

The research investigates the behavior of influential individuals across various social media platforms, aiming to understand how these behaviors differ or resemble one another based on the functionalities of the platforms. It outlines a taxonomy of social media applications, discusses the implications of user behavior on cross-platform predictions, and introduces the concept of influence homophily, highlighting how influential users on one site often connect with influential peers on others. The findings suggest the potential for clustering social media sites based on user behavior patterns and offer insights into marketing opportunities.

Related papers

The relationship between types of posts shared on Facebook and the amount of attention received by different active users
NADIA TANNOUS

2018

View PDFchevron_right
Data Analysis in Social Media
IJRASET Publication

international journal for research in applied science and engineering technology ijraset, 2020

Over the last few years, the use of social media in our everyday lives has skyrocketed. The availability of good hardware devices at affordable prices and an increase in connectivity with lower data rates at most public places has made people increasingly use social media sites. Social networking sites like Twitter, LinkedIn, Facebook, Instagram, etc. are continually developing their products to make them addictive, engaging and convenient to use for people of any age or linguistic group. All of this results in a huge chunk of data that people around the world are generating at any moment. The huge size data is one of these top-scale corporations' most valuable assets. About 2.5 quintillion bytes of data was generated every day from the usage of social media sites in 2018, and it is increasing exponentially as a result of the Internet of Things. Also, 90 percent of this data was produced only in the last 3 years. Organizations make use of this data by analysing it using different methods and generating insights into context from the same. The observations arising from this are often used for different purposes.

View PDFchevron_right
A Data Science Approach to Social Media Analytics for Discovering Topic-Specific Influential patterns from Tweets
Shashi Mogalla

International journal of computer applications, 2022

Social media services like Twitter facilitate communication among people beyond geographical boundaries on the topics / events of their current interest which might differ from time to time. Topic detection from Twitter data would be helpful to find trendy topics / products and current events for target marketing in recommender systems. The information associated with each tweet should be analyzed in three different perspectives involving textual content, social context and the temporal aspects to discover high quality clusters representing the topics and accordingly generate topic-specific influential patterns relating the users. This paper proposes a new framework for discovering topic specific influential patterns and maintaining them as snapshots along the time line. It involves topic identification through tweet clustering based on the textual content, followed by their refinement based on social context that involves information inherent in the tweeting behavior of users in addition to the timing of the tweet. Finally the framework proceeds to find the topic-specific influential patterns relating the users interested in a common topic. Dynamically finding patterns of influence enables tracking of information diffusion from influential users to their potential followers.

View PDFchevron_right
Social Media and Personalized Advertisement
Adela Calin
View PDFchevron_right
Investigation of user behavior on social networking sites
Maria Anjum

PloS one, 2017

Social networking sites (SNS) are used for social and professional interaction with people. SNS popularity has encouraged researchers to analyze the relationship of activities performed on SNS with user behavior. In doing so, the term "user behavior" is rather used ambiguously with different interpretations, which makes it difficult to identify studies on user behavior in relation to SNS. This phenomenon has encouraged this thorough research on the characteristics of user behavior being discussed in the literature. Therefore, in this study, we aim to identify, analyze, and classify the characteristics associated with user behavior to answer the research questions designed to conduct this research. A mapping study (also called scoping study), which is a type of systematic literature review, is employed to identify potential studies from digital databases through a developed protocol. Thematic analysis is carried out for the classification of user behavior. We identified 116...

View PDFchevron_right
A Review on Social Media Analysis: Challenges and Application
chetan agrawal

International Journal of Advanced Research in Computer Science, 2018

Social networks (SNs), such as Facebook, Twitter and WeChat have emerged and tightly connected web clients all over the world. By analyzing and mining social networks, we could assemble information on the comments made by people with respect to a particular product. Analysis of such comments shows its value for the design of marketing and advertising campaigns. The typical examples are viral marketing, influential bloggers finding, social advertising, social healthcare, expert finding, personalized commendation, citation networks, and so on. Social media includes interactive applications and proposals for creation, sharing and replacing client-produced matters. The earlier period of few years have brought vast escalation in social media, particularly social networking services, and it’s varying our systems to systematize and correspond. It aggregates judgments and sentiments of various clusters of group at low price. Mining the characteristics and matters of social media provides us...

View PDFchevron_right
Analytical and Technical Survey on Social Networking Sites
suvarna kanakaraddi

2020

Online social media may be a paradigm that deals with production, transfer and consumption of data. User generated content within the kind of posts, comments, likes, and tweets establish a association between the producers and also the shoppers. Content selling and social media create a good team. Social media strategy is needed to finish the content selling method. Obtaining the contents from social media is extremely abundant vital in constructing a model that may do correct prediction. The prediction results change firms to realize feedback and insight a way to improve and market the product. They will also think about their promotion in higher sense. To hold out these varieties of analysis one has got to do deep survey on social media or social networking sites to gather massive scale of information. Then one has got to determine what quite tools is accustomed do the analysis of information collected. Thus to seek out the correct findings, the paper concentrates on a review of s...

View PDFchevron_right
Social media thesis
karthik shan
View PDFchevron_right
The emergence of complex patterns in online human communication
Mogens Jensen

2011

Social media have become essential conduits in the worldwide exchange of ideas, opinions and consumer marketing. Complex networks are important tools for analyzing the information flow in many aspects of nature and human society. Here, we introduce a method based on networks and social media to gauge how ideas, opinions and new trends impact society. We show that correlations between

View PDFchevron_right
A Method for Mining Social Media to Discovering Influential Users
Journal of Computer Science IJCSIS

Influential users who diffuse information and their followers have interest to this information finally they can maximize diffusion in social networks. Influential users have different influence in diversity domain specificity for instance user may have strong influence in a special topic and another topics have weak influence. So a proposed method presented for identifying influential users based on domain specificity in this paper. This method identified influential users based on domain specificity that features of user's profile and user's actions (e.g. retweet) that influence on diffusion determined by " multiple regression " and user's contents categorized based on keywords by " TF-IDF " and finally influential users identified by " Tree Regression " based on domain specificity in this paper. The detail of this method discussed the fallowing of paper. In order to evaluate the proposed method on Twitter offer application program interface. 420 users selected randomly, they fallow their friends, join to different groups, and generated diversity tweets on Twitter. The main feature, which distinguishes this method from the previously reported methods, is in two key respective. First previous studies have quantified influence in terms of network metrics for instance number of retweet or page rank, our proposed method measured influence in terms of the size Tree Regression. Second the focuses of previous studies were based on the structural of diffusion and feature of content but Influential users have different influence in diversity domain specificity so in our proposed method focused on this feature. Results showed that accuracy of proposed method is 0.69.

View PDFchevron_right

References (22)

  1. Eytan Adar and Lada A. Adamic. Tracking information epidemics in blogspace. In WI '05: Proceedings of the The 2005 IEEE/WIC/ACM In- ternational Conference on Web Intelligence (WI'05), pages 207-214, Wash- ington, DC, USA, 2005. IEEE Computer Society.
  2. N. Agarwal, H. Liu, L. Tang, and P.S. Yu. Identifying the influential blog- gers in a community. In Proceedings of the international conference on Web search and web data mining, pages 207-218. ACM, 2008.
  3. Einat Amitay, Sivan Yogev, and Elad Yom-Tov. Serial sharers: Detect- ing split identities of web authors. In Workshop on Plagiarism Analy- sis, Authorship Identification, And Near-Duplicate Detection, Amsterdam, Netherlands, July 2007.
  4. C. Anderson. The long tail: Why the future of business is selling less of more. Hyperion Books, 2008.
  5. Rudi Cilibrasi and Paul M. B. Vitanyi. Clustering by compression. IEEE Transactions on Information Theory, 51(4):1523-1545, April 2005.
  6. M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the internet topology. In Proceedings of the conference on Applica- tions, technologies, architectures, and protocols for computer communica- tion, pages 251-262. ACM, 1999.
  7. K.E. Gill. How can we measure the influence of the blogosphere. In WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, New York. Citeseer, 2004.
  8. J. Golbeck and J. Hendler. Filmtrust: Movie recommendations using trust in web-based social networks. In Proceedings of the IEEE Consumer com- munications and networking conference, volume 96. Citeseer, 2006.
  9. J. Goldenberg, B. Libai, and E. Muller. Talk of the network: A com- plex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3):211-223, 2001.
  10. D. Gruhl, R. Guha, D. Liben-Nowell, and A.s Tomkins. Information diffu- sion through blogspace. In Proceedings of the 13th international conference on World Wide Web, pages 491-501. ACM, 2004.
  11. E. Katz. The two-step flow of communication: An up-to-date report on an hypothesis. Public Opinion Quarterly, 21(1):61-78, 1957.
  12. Ed Keller and Jon Berry. One American in ten tells the other nine how to vote, where to eat and, what to buy. They are The Influentials. The Free Press, 2003.
  13. D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influ- ence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137-146. ACM, 2003.
  14. P. Kolari, T. Finin, and A. Joshi. SVMs for the blogosphere: Blog identifi- cation and splog detection. In AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, 2006.
  15. R. Kumar et al. On the bursty evolution of blogspace. In 12th International Conference on World Wide Web, 2003.
  16. D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7):1019-1031, 2007.
  17. Yu-Ru Lin, Hari Sundaram, Yun Chi, Junichi Tatemura, and Belle L. Tseng. Detecting splogs via temporal dynamics using self-similarity analy- sis. ACM Transactions of the Web, 2(1), 2008.
  18. R.K. Merton. Social theory and social structure. Free Pr, 1968.
  19. A. Mislove, M. Marcon, K.P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, page 42. ACM, 2007.
  20. Mathew Richardson and Pedro Domingos. Mining knowledge-sharing sites for viral marketing. 2002.
  21. E.M. Rogers and F.F. Shoemaker. Communication of innovations; a cross- cultural approach. 1971.
  22. R. Zafarani and H. Liu. Connecting corresponding identities across com- munities. In Proceedings of the 3rd International Conference on Weblogs and Social Media (ICWSM09), 2009.

Related papers

Analyzing User Behavior of Social Media
IJFRCSCE Journal

In recent time, analysis the user behavior from social media sites and applications plays a important role in current scenario. The users are people who are using social media sites and applications. To analyzing the behavior researcher deals with collecting data by opinions, identifying them. Classifying data according to the orientation of opinions and presenting their behavior from data. Analysis of behavior is an interesting job but it is also a very challenging procedure. Most of social media provides huge opinions on a particular product. The response/ opinions by user play a significant role in deciding the product " s future. These responses create positive or negative impact which plays a vital role in the field of e-commerce. It provides the useful result about product which have demands in the market or not, which is very useful for both customers as well as supplier of that product. In this paper researcher purposed some steps to analyze and express their views how social media data analyze market behavior for both company and customer.

View PDFchevron_right
Influence patterns in topic communities of social media
Konstantinos Tserpes

2012

Users of Social Media typically gather into communities on the basis of some common interest. Their interactions inside these on-line communities follow several, interesting patterns. For example, they differ in the level of influence they exert to the rest of the group: some community members are actively involved, affecting a large part of the community with their actions, while the majority comprises plain participants (e.g., information consumers). Identifying users of the former category lies on the focus of interest of many recent works, as they can be employed in a variety of applications, like targeted marketing.

View PDFchevron_right
Social Networks
Zuhal akmeşe

Advances in Multimedia and Interactive Technologies, 2020

The development of technology at an incredible speed today and the fact that the internet has become an important area of social life has led to differentiation in the structure of mass communication and content production, too. This differentiation has stimulated advertisers and companies to reach the target audience through social networks with many users and different characteristics. Companies employ different strategies to be effective in these platforms. One of these strategies is collaboration with social media phenomenon. The relationship between the social networks considered as the new medium of advertising, social media phenomenon identified as influencer in these networks, and advertising is examined within the scope of this chapter. In this context, data obtained from interviews with 50 Instagram phenomenon by using semi-structured interview technique, which is a qualitative research method, were analyzed and advertising collaborations with influencers in social network...

View PDFchevron_right
Classifying and Profiling Social Media Users: An Integrated Approach
Zoe (Zoi) - Charis Belenioti, Andreas Andronikidis

The fast evolving business practice and the continuously changing users’ profiles attract researchers’ interest, such as Brandtzaeg et al. (2010), Brandtzaeg et al. (2007), Constantinides (2011) and Hsuan (2008). However, the current classification studies allows for users’ segmentation in specific social media application and only one at a time (e.g. SNS, blogs). Thus, Beemt et al. (2010) and Brandtzaeg et al. (2010) called for integrated classification researches. The study addresses the gap of users’ segmentation within all social media applications exploring how Greek users could be classified according to their “motivation of use”, “usage patterns” and “social identity” (n=270). This is the first study that attempts to classify users on the basis of their common demographics (age, gender, educational level, income, and marital status), motivations of use, behavioral patterns - such as frequency of use and usual activities-and social identity in the environment of all social media types. The present study explores users’ behavior (n=270) by providing a classification scheme along with a detailed profiling of the resulted clusters. Implementing cluster analysis results indicated that users can be classified into three categories (“Information Seekers”, 27%, - “Operational and Psychological Boost Benefits Seekers”, 47 %- “Communication Seekers”, 26%). The “social identity” factor was also used through Anova Test presenting noteworthy differences among the emerging clusters. The current research contributes to develop a users’ classification scheme treating social media as one single category. The paper ends by providing theoretical and managerial implications serving helpful insights about social media patterns.

View PDFchevron_right
Empirical Analysis of User Behavior in Social Media
Santosh K Ray, Mohammed Saeed

Social media has become an integrated part of the modern life-as-it has become one of the main instruments for sharing information, helping people, getting in touch with other people. Billions of people are using social networking websites such as Facebook, Twitter, and LinkedIn and generating huge amounts of data every day. Teaching,-particularly in higher education, has become more student-centered triggering the need to employ social media technologies where students can be linked to their institutions to have access to teaching material besides news and announcements. Analyzing the behavior of the users on social media is a complex task. In this paper, we present empherical analyze of some of the important aspects of user's behavior on social networking highlighting many interesting aspects of this type of user's behavior. This paper is a first part of a series of research work investigating the implementation of mobile learning through the use of social media technologies in teaching and learning in higher education sector.

View PDFchevron_right

Related topics

  • Behavior computing

    • Cited by

    Social Media and Security
    Samer Al-Khateeb

    Encyclopedia of Big Data

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved