Facebook in context(s): Measuring emotional responses across time and space

Bayer, Joseph B.; Ellison, Nicole B.; Schoenebeck, Sarita; Brady, Erin; Falk, Emily B.

doi:10.1177/1461444816681522

Cited by 92 publications

(86 citation statements)

References 53 publications

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“…This means that potential customers see personalized advertisements on Facebook that are based on demographic data, interests and behavior. When consumers use mobile phones, Facebook can locate consumers through the global positioning system (GPS) [3]. For example, it is possible to target a particular location where Facebook users have recently been.…”

Section: Facebook Advertisements Personal Targeting Ofmentioning

confidence: 99%

Facebook Marketing Intelligence

Groothuis¹,

Spil²,

Effing³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Facebook marketing is becoming an increasingly important tool for companies to influence consumer decision-making. However, there is currently little empirical knowledge about the extent of influence of Facebook marketing on the decision-making process of consumers. This study contributes to these gaps in the literature and investigates the influence of Facebook marketing activities on the decision-making process of consumers. The theory revealed four Facebook marketing activities that affected the first two phases of the decision-making process. These Facebook marketing activities were advertisements, recommend/share, likes and reviews. Whether they actually had an impact has been tested with the help of survey among 112 respondents. The results of the regression analysis showed that all four Facebook marketing activities had a positive influence on the decision-making process.

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Section: Facebook Advertisements Personal Targeting Ofmentioning

confidence: 99%

Facebook Marketing Intelligence

Groothuis¹,

Spil²,

Effing³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…ACM/IEEE, June 2019, Urbana-Champaign, Illinois USA © 2019 Association for Computing Machinery. the knowledge online platforms provide about human behavior-an area of research known as social-media analytics [3,6,9]. Studies in social-media analytics tend to focus either on text, using approaches such as Natural Language Processing (NLP), sentiment analysis, or opinion mining to arrive at and support research conclusions [12], or on the proliferation of content through online communities [5].…”

Section: Introductionmentioning

confidence: 99%

Shared Feelings: Understanding Facebook Reactions to Scholarly Articles

Freeman

Roy

Fattoruso

et al. 2019

2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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Research on social-media platforms has tended to rely on textual analysis to perform research tasks. While text-based approaches have significantly increased our understanding of online behavior and social dynamics, they overlook features on these platforms that have grown in prominence in the past few years: click-based responses to content. In this paper, we present a new dataset of Facebook Reactions to scholarly content. We give an overview of its structure, analyze some of the statistical trends in the data, and use it to train and test two supervised learning algorithms. Our preliminary tests suggest the presence of stratification in the number of users following pages, divisions that seem to fall in line with distinctions in the subject matter of those pages.

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“…The research we do is not only relevant for advancing our theories within the discipline, it also has the power to influence policies and practices throughout the world (Giner-Sorolla, 2019;Goroff, Lewis, Scheel, Scherer, & Tucker, 2018;. We study how the shows our children watch influence what they learn (e.g., Bonus & Mares, 2018;Ward, 2002), and the implications of those processes for practices like representation in the media (e.g., Dal Cin, Stoolmiller, & Sargent, 2013); how the way we frame messages about climate change affects the public's opinion about one of the most critical issues of our time (e.g., Schuldt, Enns, & Cavaliere, 2017); how and why health messages influence people's smoking decisions (e.g., Skurka et al, 2018); how the technology we use influences our abilities to communicate and collaborate effectively across cultures (e.g., Yuan, Liao, & Bazarova, 2019), and whether the use of different technologies have any consequences for our health and well-being (e.g., Bayer, Ellison, Schoenebeck, Brady, & Falk, 2018;Orben & Przybylski, 2019).…”

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confidence: 99%