Perceptions of Social Media's Relevance and Targeted Advertisements

Baglione, Stephen L.; Tucci, Louis A.

doi:10.1080/10496491.2018.1443312

Cited by 13 publications

(9 citation statements)

References 49 publications

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“…After that, each node equally shares PR value with outgoing neighbors. In mathematics, PR value of each node at t step is calculated as follows in Equation 4.…”

Section: Pagerank Centralitymentioning

confidence: 99%

“…A node with value 0 means node is disconnected from rest of the network or having no adjacent neighbor node. 4 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 01 2020 -05 2020 | Volume 7 | Issue 23 | e2…”

Section: Shortest Path: Consideringmentioning

confidence: 99%

“…So, social network is house of * Corresponding author. Email: writetosamadalvi@gmail.com spreading news [21], marketing product [44], targeting groups [4] etc. To carry out these types of tasks in social network, identifying the central or influential or significant node is challenging task.…”

Section: Introductionmentioning

confidence: 99%

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SAM Centrality: a Hop-Based Centrality Measure for Ranking Users in Social Network

Samad¹,

Qadir²,

Nawaz³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Majority of researcher are attracted by the social network analysis due to the rush of people towards social network. Along with many problems, social network analysis is facing an interesting problem that is ranking of users in social network which is gaining more attention due to the increasing number of social users. Measuring centrality of nodes in a social graph, have been important issue in social network analysis. Lot of centrality methods have been proposed in this regard. In this paper, hop based centrality measures called SAM is purposed. To investigate the measure, we applied on various dataset. In comparisons, on all these social graphs, we obtain better results than other centrality measures (i.e., Degree, PageRank, Betweeness and Closeness) using SIR model.

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“…After that, each node equally shares PR value with outgoing neighbors. In mathematics, PR value of each node at t step is calculated as follows in Equation 4.…”

Section: Pagerank Centralitymentioning

confidence: 99%

Section: Shortest Path: Consideringmentioning

confidence: 99%

See 1 more Smart Citation

SAM Centrality: a Hop-Based Centrality Measure for Ranking Users in Social Network

Samad¹,

Qadir²,

Nawaz³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

show abstract

“…In research that specifically examined Facebook advertising, much of the attention has been concentrated on audience motivations and message strategy. While a few studies indicated somewhat negative reception to Facebook advertising, especially privacy concerns related to targeted messages (Baglione and Tucci, 2019; Barreto, 2013), many scholars reported positive effects of Facebook advertising on advertising and brand responses (De Keyzer et al , 2015; Duffett, 2015; Schivinski and Dabrowski, 2015). In general, Facebook advertising is more effective when it offers informative and engaging content, especially content related to brand-personality attributes or user-generated content (Kim et al , 2016; Lee et al , 2018; Schivinski and Dabrowski, 2015), targets more engaged audiences such as those who regularly participate in online activities or follow brand pages (Celebi, 2015; Kim et al , 2016), and when the advertising message takes advantage of peer influence and personal relevance (De Keyzer et al , 2015; Jung et al , 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Facebook news feed ads: a social impact theory perspective

Xue

2019

JRIM

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Purpose The purpose of this paper is to examine the effects of “social information” in Facebook News Feed ads on American users’ advertising responses, including ad credibility, attitude-toward-the-ad, brand interest, intention to click and purchase intention. Using social impact theory as a conceptual framework, three factors were tested – relationship strength, physical distance and number of affiliated friends. The moderating role of product involvement was also investigated. Design/methodology/approach A 2 (strength of relationship: weak vs strong) × 2 (immediacy of relationship: close distance vs long distance) × 2 (number of friends: one vs several) between-group factorial design was used, and 397 research participants were recruited through Amazon’s Mechanical Turk (MTurk). Findings Significant main effects were found for relationship strength and physical distance. Product involvement was identified as a moderating variable. No significant effects were found under the high involvement condition. Under the low involvement condition, however, relationship strength and physical distance significantly affected Facebook users’ advertising responses. Research limitations/implications Research samples were recruited through Amazon’s Mechanical Turk (MTurk). It is possible that the characteristics of this specific social group might have influenced the findings of the study. Only one specific product category, fast casual restaurant, was tested. Participants were asked to imagine themselves in certain scenarios. Even though the scenarios were carefully tested in the pretest and clear instructions were given, field experiments might be helpful in future research to better reflect the actual consumer experience. Practical implications Marketers should take advantage of the “social information” feature in Facebook News Feed ads, especially for low involvement products. Names of friends with stronger social relationships and within close physical distance should be included in the ads. Originality/value The study is one of the first to examine the effects of “social information” in Facebook advertising. It also confirms the Social Impact Theory in a social media setting.

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“…Even the largest, most heavily advertised brands enjoy shockingly small levels of user engagement (Sitta et al 2018). Conversely, most users do not regard such advertising as “useful, meaningful, and leading to purchase intentions” (Baglione and Tucci 2018; Greenlight 2012). Nor do so-called “multi-touch” strategies—themselves devised to compensate for the shortcomings of paid advertising (Einstein 2016)—fare much better, as advertiser efforts to repeatedly engage users typically backfire, driving them away from the products or brands on offer (Baltas 2003; Brettel et al 2015).…”

Section: Advertising Mythsmentioning

confidence: 99%

The Self-consuming Commodity: Audiences, Users, and the Riddle of Digital Labor

Kaplan

2019

Television & New Media

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This article reexamines the digital labor debate in light of its inheritance of the “audience commodity” problematic. It argues that prevailing approaches to the problem of digital labor proceed from a crucial misunderstanding of the economic status and function of advertising in general and in the social media industry in particular. To remedy this problem, it offers an analysis of social media systems as market monopolies that organize a self-defeating arms race among their customers. This arms race enables social networking sites (SNS) to extract large price surpluses, effectively exploiting their customers.

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Perceptions of Social Media's Relevance and Targeted Advertisements

Cited by 13 publications

References 49 publications

SAM Centrality: a Hop-Based Centrality Measure for Ranking Users in Social Network

SAM Centrality: a Hop-Based Centrality Measure for Ranking Users in Social Network

Facebook news feed ads: a social impact theory perspective

The Self-consuming Commodity: Audiences, Users, and the Riddle of Digital Labor

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