ECO-PSYCHOLOGICAL ANALYSIS OF BIMBOS BELALANG SONG LYRIC: A DISCOURSE PERSPECTIVE

The current study investigated Bimbos Song lyric entitled Belalang in a Discourse Perspective. It has come to be known that Bimbo is a senior music group, specializing in deep philosophical touches of romantic songs. Belalang is a song attempting to create an analog of an insect (mantis) with special mating behaviors to human love affairs tied in dangerous wedlock. The lyric was analyzed in two ways, (1) to find out the field, tenor and mode of the discourse, employing the analytical framework of Systemic Functional Linguistics (SFL) in a macro sense, and (2) to find out the eco-psychological phenomena, employing the analytical framework of eco-psychology. The findings show that both mantis (with its special mating behavior) and humans (in showing true love to the destructive wife) have a similar patternto preserve the ecosystem or in other words to create a conducive environment. The moral teaching seems to support the old saying that to love does not necessarily own despite the presence of love-chemistry. A seemingly peaceful marital life has to come to its end due to an unexpected mystical curse of Bahu Laweyan. Yet, secondary love can be made possible in search of peaceful lives, to avoid self-destruction

Facebook: The Business of Web Ads and Data Processing

Treball Final de Grau en Economia. Codi: EC1049. Curs acadèmic: 2021/2022This paper focuses on user information on the internet, specifically that which is available to Meta along with a review of how it is obtained and classified for marketing. Privacy policies and cookies for purposes other than those of the consumer along with the scandals and controversies surrounding this. A review of the historical context to get to the great power available to the company, the regulations that accompany this process and its hardening in the wake of events in which Meta has been involved. Highlighting the voluminous data leak by Cambridge Analytica through its association with Facebook for subsequent use for political purposes among others. And the consequences of this. Followed by a review of the European framework and the direction it decides to take in the near future with regard to the rapid advances in technology and the need to protect people in their browsing on the web. Finally, a reflection on the reality we live in, the misinformation, numerous empty stimuli accompanied by unnecessary consumerism and how unprotected we really are. In order to give our time and security the importance it deserves

Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics. We noticed that not all political ads we detected were present in the Facebook Ad Library for political ads. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms

Platform power in the video advertising ecosystem

Platform power is a societal concern on many levels. Thus, we argue that addressing it with the common market competition approach is limited. The research we present aims to develop and test an alternative approach to conceptualising and assessing platform power. We propose a framework that operationalises the concept of the integrated platform ecosystem by bounding it with theories of harm to citizen wellbeing. Applying it, instead of defining a market, we use a specific, novel theory of harm to define the audiovisual advertising ecosystem. Our investigation into the dynamics of this ecosystem and conditions shaping them incorporated elite interviews with representatives of firms involved, document analysis and an examination of legal frameworks in a sample of four European jurisdictions. The evidence we present points to an inherent bias in the opacity of trading and to systemic advantage in relationship building, as well as potential power imbalances at ‘nodes’ where data is used for targeting, planning, and metrics. We discuss the policy implications of these findings and suggest specific questions for regulators to be asking

Understanding the Complexity of Detecting Political Ads

Online political advertising has grown significantly over the last few years. To monitor online sponsored political discourse, companies such as Facebook, Google, and Twitter have created public Ad Libraries collecting the political ads that run on their platforms. Currently, both policymakers and platforms are debating further restrictions on political advertising to deter misuses. This paper investigates whether we can reliably distinguish political ads from non-political ads. We take an empirical approach to analyze what kind of ads are deemed political by ordinary people and what kind of ads lead to disagreement. Our results show a significant disagreement between what ad platforms, ordinary people, and advertisers consider political and suggest that this disagreement mainly comes from diverging opinions on which ads address social issues. Overall our results imply that it is important to consider social issue ads as political, but they also complicate political advertising regulations.Comment: Proceedings of the Web Conference 2021 (WWW '21), April 19--23, 2021, Ljubljana, Sloveni

Target(ed) Advertising

Targeted advertising—using data about consumers to customize the ads they receive—is deeply controversial. It also creates a regulatory quandary. Targeted ads generate more money than untargeted ones for apps and online platforms. Apps and platforms depend on this revenue stream to offer free services to users, if not for their financial viability altogether. However, targeted advertising also generates significant privacy risks and consumer resentment. Despite sustained attention to this issue, neither legal scholars nor policymakers have crafted interventions that address both concerns, and existing regulatory regimes for targeted advertising have critical gaps. This Article makes three key contributions to the targeted advertising literature. First, it rigorously interrogates the empirical evidence on the effectiveness of the practice, concluding that targeted ads generate important benefits for firms, but mixed effects for society. Next, it evaluates the risks and harms of these ads and maps them to existing regulatory regimes to identify gaps. Finally, it elucidates a co-regulatory reform proposal that combines industry expertise with oversight by the Federal Trade Commission to address invasive data collection techniques, insecure data storage, and problematic transactions in consumer data. The proposal closes regulatory loopholes, reduces information asymmetry for enforcement in a fast-changing industry, and offers a pragmatic path to implementation

Collaborative Ad Transparency: Promises and Limitations

International audienceSeveral targeted advertising platforms offer transparency mechanisms, but researchers and civil societies repeatedly showed that those have major limitations. In this paper, we propose a collaborative ad transparency method to infer, without the cooperation of ad platforms, the targeting parameters used by advertisers to target their ads. Our idea is to ask users to donate data about their attributes and the ads they receive and to use this data to infer the targeting attributes of an ad campaign. We propose a Maximum Likelihood Estimator based on a simplified Bernoulli ad delivery model. We first test our inference method through controlled ad experiments on Facebook. Then, to further investigate the potential and limitations of collaborative ad transparency, we propose a simulation framework that allows varying key parameters. We validate that our framework gives accuracies consistent with real-world observations such that the insights from our simulations are transferable to the real world. We then perform an extensive simulation study for ad campaigns that target a combination of two attributes. Our results show that we can obtain good accuracy whenever at least ten monitored users receive an ad. This usually requires a few thousand monitored users, regardless of population size. Our simulation framework is based on a new method to generate a synthetic population with statistical properties resembling the actual population, which may be of independent interest