Empirical analysis of implicit brand networks on social media

Zhang, Xiaoying; Bhattacharyya, Siddhartha; Ram, Sudha

doi:10.1145/2631775.2631806

Cited by 4 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They have performed social recommendation on this network and their experimental results show that use of implicit user relation in social recommendation methods generate almost identical preferences as explicit trust values. Zhang et al (2014) proposed a methodology to design a implicit brand network from the dataset consisting of historical activities of users on a social media platform. Their experiments answer many interesting research questions about the topology of the brand network, number of users in an influential brand etc.…”

Section: Design and Analysis Of Implicit Social Networkmentioning

confidence: 99%

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Banerjee¹

2020

Preprint

View full text Add to dashboard Cite

Implicit Social Network is a connected social structure among a group of persons, where two of them are linked if they have some common interest. One real-life example of such networks is the implicit social network among the customers of an online commercial house, where there exist an edge between two customers if they like similar items. Such networks are often useful for different commercial applications such as target advertisement, viral marketing, etc. In this article, we study two fundamental problems in this direction. The first one is that, given the user-item rating data of an E-Commerce house, how we can design implicit social networks among its users and the second one is at the time of designing itself can we obtain the connectivity information among the users. Formally, we call the first problem as the Implicit User Network Design Problem and the second one as Implicit User Network Design with Connectivity Checking Problem. For the first problem, we propose three different algorithms, namely 'Exhaustive Search Approach', 'Clique Addition Approach', and 'Matrix Multiplication-Based Approach'. For the second problem, we propose two different approaches. The first one is the sequential approach: designing and then connectivity checking, and the other one is a concurrent approach, which is basically an incremental algorithm that performs designing and connectivity checking simultaneously. Proposed methodologies have experimented with three publicly available rating network datasets such as Flixter, Movielens, and Epinions. Reported computational time shows that the 'Clique Addition Approach' is the fastest one for designing the implicit social network. For designing and connectivity check-The author is supported by the Post Doctoral Fellowship Grant provided by Indian Institute of Technology Gandhinagar (Project No. MIS/IITGN/PD-SCH/201415/006). A small part of this study has been previously published as Banerjee et al. (2017).

show abstract

Section: Design and Analysis Of Implicit Social Networkmentioning

confidence: 99%

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Banerjee¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The role of user-generated content for ranking and recommendation tasks is studied in diverse research scenarios. Zhang et al [37] examine brands by incorporating users posts and information about users interactions. Their experiments on Facebook data show that negative comments generate greater awareness of a brand than positive comments.…”

Section: Related Workmentioning

confidence: 99%

Mining Affective Context in Short Films for Emotion-Aware Recommendation

Orellana-Rodriguez

Diaz-Aviles

Nejdl³

2015

Proceedings of the 26th ACM Conference on Hypertext &Amp; Social Media - HT '15

View full text Add to dashboard Cite

Emotion is fundamental to human experience and impacts our daily activities and decision-making processes where, e.g., the affective state of a user influences whether or not she decides to consume a recommended item -movie, book, product or service. However, information retrieval and recommendation tasks have largely ignored emotion as a source of user context, in part because emotion is difficult to measure and easy to misunderstand. In this paper we explore the role of emotions in short films and propose an approach that automatically extracts affective context from user comments associated to short films available in YouTube, as an alternative to explicit human annotations. We go beyond the traditional polarity detection (i.e., positive/negative), and extract for each film four opposing pairs of primary emotions: joy-sadness, anger-fear, trust-disgust, and anticipationsurprise. Finally, in our empirical evaluation, we show how the affective context extracted automatically can be leveraged for emotion-aware film recommendation.

show abstract

“…Text mining provides an interesting approach for elucidating knowledge from raw data. Its application in Facebook has been studied, as a part of social media monitoring (Cvijikj & Michahelles, 2011), to understand the impact of sentiments on brand awareness (Zhang, Bhattacharyya, & Ram, 2014) and target selection for online advertising (Zhang et al, 2016). Similar to these studies, we apply sentiment mining to understand user interaction that enables marketers to design an appropriate marketing communication on social media platform.…”

Section: Introductionmentioning

confidence: 99%

Decoding User Interaction Dynamics on Facebook Fan Page: A Sentiment Mining Approach

Chandrasekaran

2019

Global Business Review

View full text Add to dashboard Cite

Increased presence of firms on social media like Facebook has enabled users to interact directly with the firms. As this platform is used for brand promotions, it is important for firms to understand the dynamics of user interaction that happens on the brand’s fan page. This study investigates the influence of marketer-generated content (MGC) and the recursive effect of sentiments on user interaction with the help of sentiment mining approach and negative binomial regression. The analysis is done over 2,121 marketers’ content and 22 million user content across the top five Facebook brand pages in India. The results show that an increase in positive comment sentiment triggers increased liking and sharing behaviour by the users on the marketers’ content. Moreover, users are more inclined to like and share on marketers’ contents that are informative posts. Posts containing images garner higher likes and comments, but videos have higher shares. The results are useful for the brands to understand user behaviour on a brand’s social media fan page.

show abstract

Empirical analysis of implicit brand networks on social media

Cited by 4 publications

References 33 publications

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Mining Affective Context in Short Films for Emotion-Aware Recommendation

Decoding User Interaction Dynamics on Facebook Fan Page: A Sentiment Mining Approach

Contact Info

Product

Resources

About