You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction

In the cost per click (CPC) pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for "valueless" clicks, or so-called accidental clicks. [...] Charging advertisers for such clicks is detrimental in the long term as the advertiser may decide to run their campaigns on other ad networks. In addition, machine-learned click models trained to predict which ad will bring the highest revenue may overestimate an ad click-through rate, and as a consequence negatively impacting revenue for both the ad network and the publisher. In this work, we propose a data-driven method to detect accidental clicks from the perspective of the ad network. We collect observations of time spent by users on a large set of ad landing pages - i.e., dwell time. We notice that the majority of per-ad distributions of dwell time fit to a mixture of distributions, where each component may correspond to a particular type of clicks, the first one being accidental. We then estimate dwell time thresholds of accidental clicks from that component. Using our method to identify accidental clicks, we then propose a technique that smoothly discounts the advertiser's cost of accidental clicks at billing time. Experiments conducted on a large dataset of ads served on Yahoo mobile apps confirm that our thresholds are stable over time, and revenue loss in the short term is marginal. We also compare the performance of an existing machine-learned click model trained on all ad clicks with that of the same model trained only on non-accidental clicks. There, we observe an increase in both ad click-through rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when using the latter. [...

You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction

In the cost per click pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for “valueless” clicks, or so-called accidental clicks. These happen when users click on an ad, are redirected to the advertiser website and bounce back without spending any time on the ad landing page. Charging advertisers for such clicks is detrimental in the long term as the advertiser may decide to run their campaigns on other ad networks. In addition, machine-learned click models trained to predict which ad will bring the highest revenue may overestimate an ad click-through rate, and as a consequence negatively impacting revenue for both the ad network and the publisher. In this work, we propose a data-driven method to detect accidental clicks from the perspective of the ad network. We collect observations of time spent by users on a large set of ad landing pages—i.e., dwell time. We notice that the majority of per-ad distributions of dwell time fit to a mixture of distributions, where each component may correspond to a particular type of clicks, the first one being accidental. We then estimate dwell time thresholds of accidental clicks from that component. Using our method to identify accidental clicks, we then propose a technique that smoothly discounts the advertiser’s cost of accidental clicks at billing time. Experiments conducted on a large dataset of ads served on Yahoo mobile apps confirm that our thresholds are stable over time, and revenue loss in the short term is marginal. We also compare the performance of an existing machine-learned click model trained on all ad clicks with that of the same model trained only on non-accidental clicks. There, we observe an increase in both ad click-through rate (+ 3.9%) and revenue (+ 0.2%) on ads served by the Yahoo Gemini network when using the latter. These two applications validate the need to consider accidental clicks for both billing advertisers and training ad click models

Evaluating the Effectiveness of Counter-Narrative Tactics in Preventing Radicalization

The U.S. Department of State disseminates counter-radicalization information through social media but has been unable to reach users due to an inability to create engaging posts due to a lack of understanding of the interests of the general population. The purpose of this quantitative study was to assess the utility of data analytics when administering counter-radicalization social media campaigns. The population for this study were social media posts published on the Quilliam Facebook page between 1 January 2018 and 31 December 2018. The nonexperimental quantitative descriptive research design sought to examine the correlation between the independent variables (topic of a post, use of visual aids in the post, and the geopolitical region the post addresses) and the dependent variables (resulting likes and shares). This study relied on the strategic choice theory which argues that individuals perform a cost and benefit analysis when deciding to join a terrorist organization and commit acts of terrorism. Specifically, individuals are often interested in participating in terror-ism in an effort to gain resources and feel a sense of belonging but can be dissuaded upon realization that terrorism can actually degrade their quality of life. The research found that social media can be used as a tool to increase the perceived costs of terrorism and decrease the perceived benefits of terrorism. The study concluded that posts which involved a personal story emphasizing the ramifications of terrorism and included a video resulted in the highest number of likes and shares, respectively. The findings provide a strong argument for utilizing data analytics to improve the dissemination of counter-radicalization information which could prevent individuals from joining terrorist organizations and committing acts of terrorism