Measuring Ethnic Preferences in Bosnia and Herzegovina with Mobile Advertising

Nisser, Annerose; Weidmann, Nils B.

doi:10.1371/journal.pone.0167779

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Only two studies truly leveraged the global reach of the app store and run the experiments entirely through the app. One study recruited 18,420 participants and examined momentary subjective well-being as a result of different game designs (Rutledge et al, 2014) and the other delivered experiment manipulations as in-app banner advertisements and examined ethnic preferences in voting among 44,000 unique users (Nisser & Weidmann, 2016). Considering diversity of participants, only six studies targeted non-White populations and only one study targeted lower SES communities.…”

Section: Resultsmentioning

confidence: 99%

“…Only one study reported the cost of the study and the ethical concern from the institutional review board (IRB). Nisser and Weidmann (2016) reported a budget of $5,000 and reached 44,000 unique users through in-app advertisements. IRB granted a waiver of ethical approval because the display of banner ads within apps and the collection of location data are standard practices in mobile advertising.…”

Section: Resultsmentioning

confidence: 99%

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Advantages and challenges in using mobile apps for field experiments: A systematic review and a case study

Zhang

Calabrese

Ding

et al. 2017

Mobile Media & Communication

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As smartphone’s computing power continues to grow and as mobile applications (apps) continue to dominate digital engagement, apps have become a new frontier for advancing field experiment methodology. Using apps may help researchers to scale up the reach, precisely control randomization and experiment materials, collect a variety of objective and self-reported data over time, and more conveniently replicate and adapt an experiment. We performed a systematic review on field experiments involving apps published between 2007 and 2017. Seven databases were scanned using a predefined search strategy. The database search retrieved 4,810 citations; 101 articles met the inclusion criteria. Our review suggests that scholars have only started to employ apps in field experiments in the last 4 years. Most studies only used apps as an experiment treatment instead of an experiment platform; therefore, researchers have yet to fully leverage the advantages. Almost all studies were from the health research domain and 77.2% used randomized controlled trial design. Only 7 studies utilized smartphone sensors for collecting data. Only one study reported cost and ethical concerns regarding using apps for the experiment. Given these findings, we reported a case study that targeted a minority racial group and leveraged the advantages of apps as an experiment platform and as a data collection tool to illustrate practical challenges and lessons learned regarding time, financial cost, and technical support. In conclusion, we suggest apps provide new ways to study causal mechanisms with experiment big data. Limitations of generalizability, retention, and design quality were discussed as well.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Advantages and challenges in using mobile apps for field experiments: A systematic review and a case study

Zhang

Calabrese

Ding

et al. 2017

Mobile Media & Communication

View full text Add to dashboard Cite

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“…With the invent of smart-phones, people have started using it addictively in their daily life. On the other hands, as the digital advertising industry business model rely on the people usage of computing devices, even they have shifted their concentration on the mobile platform with the transformation of promoting ads through in-app advertisement [26]. In their business model, "click and impression" plays a signiicant role as it is one of the parameters to calculate the payment for the publisher by the ad network.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Model to Fight Against Ad Click Fraud

Thejas

Boroojeni

Chandna

et al. 2019

Proceedings of the 2019 ACM Southeast Conference

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Click fraud is a fast-growing cyber-criminal activity with the aim of deceptively clicking on the advertisements to make the proit to the publisher or cause loss to the advertiser. Due to the popularity of smartphones since the last decade, most of the modern-day advertisement businesses have been shifting their focus toward mobile platforms. Nowadays, in-app advertisement on mobile platforms is the most targeted victim of click fraud. Malicious entities launch attacks by clicking ads to artiicially increase the click rates of speciic ads without the intention of using them for legitimate purposes. The fraud clicks are supposed to be caught by the ad providers as part of their service to the advertisers; however, there is a lack of research in the current literature for addressing and evaluating different techniques of click fraud detection and prevention. Another challenge toward click fraud detection is that the attack model can itself be an active learning system (smart attacker) with the aim of actively misleading the training process of fraud detection model via polluting the training data. In this paper, we propose a deep-learning based model to address the challenges as mentioned above. The model is a hybrid of artiicial neural network (ANN), auto-encoder and semi-supervised generative adversarial network (GAN). Our proposed approach triumphs excellent accuracy than other models. CCS CONCEPTS· Information systems → Online advertising; · Security and privacy → Intrusion/anomaly detection and malware mitigation; · Computing methodologies → Machine learning.

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