Big Brother is Listening to You: Digital Eavesdropping in the Advertising Industry

In the Digital Age, information is more accessible than ever. Unfortunately, that accessibility has come at the expense of privacy. Now, more and more personal information is in the hands of corporations and governments, for uses not known to the average consumer. Although these entities have long been able to keep tabs on individuals, with the advent of virtual assistants and “always-listening” technologies, the ease by which a third party may extract information from a consumer has only increased. The stark reality is that lawmakers have left the American public behind. While other countries have enacted consumer privacy protections, the United States has no satisfactory legal framework in place to curb data collection by greedy businesses or to regulate how those companies may use and protect consumer data. This Article contemplates one use of that data: digital advertising. Inspired by stories of suspiciously well-targeted advertisements appearing on social media websites, this Article additionally questions whether companies have been honest about their collection of audio data. To address the potential harms consumers may suffer as a result of this deficient privacy protection, this Article proposes a framework wherein companies must acquire users\u27 consent and the government must ensure that businesses do not use consumer information for harmful purposes

Mapping and analysis of the current self- and co- regulatory framework of commercial communication aimed at minors

As the advertising sector has been very active in self-regulating commercial communication aimed at children, a patchwork of different rules and instruments exist, drafted by different self-regulatory organisations at international, European and national level. In order to determine the scope and contents of these rules, and hence, the actual level of protection of children, a structured mapping of these rules is needed. As such, this report aims to provide an overview of different categories of Alternative Regulatory Instruments(ARIs,such as self- and co-regulation regarding (new) advertising formats aimed at children. This report complements the first legal AdLit research report, which provided an overview of the legislative provisions in this domain.status: publishe

AdPExT: designing a tool to assess information gleaned from browsers by online advertising platforms

The world of online advertising is directly dependent on data collection of the online browsing habits of individuals to enable effective advertisement targeting and retargeting. However, these data collection practices can cause leakage of private data belonging to website visitors (end-users) without their knowledge. The growing privacy concern of end-users is amplified by a lack of trust and understanding of what and how advertisement trackers are collecting and using their data. This paper presents an investigation to restore the trust or validate the concerns. We aim to facilitate the assessment of the actual end-user related data being collected by advertising platforms (APs) by means of a critical discussion but also the development of a new tool, AdPExT (Advertising Parameter Extraction Tool), which can be used to extract third-party parameter key-value pairs at an individual key-value level. Furthermore, we conduct a survey covering mostly United Kingdom-based frequent internet users to gather the perceived sensitivity sentiment for various representative tracking parameters. End-users have a definite concern with regards to advertisement tracking of sensitive data by global dominating platforms such as Facebook and Google

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

Online tracking: Questioning the power of informed consent

Online tracking technologies have raised considerable concerns regarding privacy and the protection of personal data of users. In order to help users to regain control over their personal data, Europe has amended its ePrivacy directive towards an opt-in regime. There are however many open questions concerning its implementation, especially regarding the issue of informed consent. This paper explores how the new legal situation impacts on behavioral advertising practices via the storing and reading of cookies in the Netherlands. The results show that the majority of the surveyed parties involved in behavioural advertising do not inform users about the storing of cookies or the purposes of data processing of the subsequently obtained data, neither do they have obtained users' consent for the storage of cookies. We also found that the majority of users lack the skills and knowledge how to handle cookies. These findings critically question the wisdom of the informed consent regime which lies currently at the heart of Europe's ePrivacy directive. --Online behavioural advertising,profiling,cookies,informed consent,Do Not Track,ePrivacy Directive

Context aware advertising

IP Television (IPTV) has created a new arena for digital advertising that has not been explored to its full potential yet. IPTV allows users to retrieve on demand content and recommended content; however, very limited research has been applied in the domain of advertising in IPTV systems. The diversity of the field led to a lot of mature efforts in the fields of content recommendation and mobile advertising. The introduction of IPTV and smart devices led to the ability to gather more context information that was not subject of study before. This research attempts at studying the different contextual parameters, how to enrich the advertising context to tailor better ads for users, devising a recommendation engine that utilizes the new context, building a prototype to prove the viability of the system and evaluating it on different quality of service and quality of experience measures. To tackle this problem, a review of the state of the art in the field of context-aware advertising as well as the related field of context-aware multimedia have been studied. The intent was to come up with the most relevant contextual parameters that can possibly yield a higher percentage precision for recommending advertisements to users. Subsequently, a prototype application was also developed to validate the feasibility and viability of the approach. The prototype gathers contextual information related to the number of viewers, their age, genders, viewing angles as well as their emotions. The gathered context is then dispatched to a web service which generates advertisement recommendations and sends them back to the user. A scheduler was also implemented to identify the most suitable time to push advertisements to users based on their attention span. To achieve our contributions, a corpus of 421 ads was gathered and processed for streaming. The advertisements were displayed in reality during the holy month of Ramadan, 2016. A data gathering application was developed where sample users were presented with 10 random ads and asked to rate and evaluate the advertisements according to a predetermined criteria. The gathered data was used for training the recommendation engine and computing the latent context-item preferences. This also served to identify the performance of a system that randomly sends advertisements to users. The resulting performance is used as a benchmark to compare our results against. When it comes to the recommendation engine itself, several implementation options were considered that pertain to the methodology to create a vector representation of an advertisement as well as the metric to use to measure the similarity between two advertisement vectors. The goal is to find a representation of advertisements that circumvents the cold start problem and the best similarity measure to use with the different vectorization techniques. A set of experiments have been designed and executed to identify the right vectorization methodology and similarity measure to apply in this problem domain. To evaluate the overall performance of the system, several experiments were designed and executed that cover different quality aspects of the system such as quality of service, quality of experience and quality of context. All three aspects have been measured and our results show that our recommendation engine exhibits a significant improvement over other mechanisms of pushing ads to users that are employed in currently existing systems. The other mechanisms placed in comparison are the random ad generation and targeted ad generation. Targeted ads mechanism relies on demographic information of the viewer with disregard to his/her historical consumption. Our system showed a precision percentage of 69.70% which means that roughly 7 out of 10 recommended ads are actually liked and viewed to the end by the viewer. The practice of randomly generating ads yields a result of 41.11% precision which means that only 4 out of 10 recommended ads are actually liked by viewers. The targeted ads system resulted in 51.39% precision. Our results show that a significant improvement can be introduced when employing context within a recommendation engine. When introducing emotion context, our results show a significant improvement in case the user’s emotion is happiness; however, it showed a degradation of performance when the user’s emotion is sadness. When considering all emotions, the overall results did not show a significant improvement. It is worth noting though that ads recommended based on detected emotions using our systems proved to always be relevant to the user\u27s current mood

Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness

Retailers gather data about customers' online behavior to develop personalized service offers. Greater personalization typically increases service relevance and customer adoption, but paradoxically, it also may increase customers' sense of vulnerability and lower adoption rates. To demonstrate this contradiction, an exploratory field study on Facebook and secondary data about a personalized advertising campaign indicate sharp drops in click-through rates when customers realize their personal information has been collected without their consent. To investigate the personalization paradox, this study uses three experiments that confirm a firm's strategy for collecting information from social media websites is a crucial determinant of how customers react to online personalized advertising. When firms engage in overt information collection, participants exhibit greater click-through intentions in response to more personalized advertisements, in contrast with their reactions when firms collect information covertly. This effect reflects the feelings of vulnerability that consumers experience when firms undertake covert information collection strategies. Trust-building marketing strategies that transfer trust from another website or signal trust with informational cues can offset this negative effect. These studies help unravel the personalization paradox by explicating the role of information collection and its impact on vulnerability and click-through rates