To believe or not to believe

None provided

To Believe or Not to Believe: Voters’ Responses to Sexual Assault Allegations in Politics

Since the viral 2017 #MeToo movement, public opinion on cases of sexual misconduct has been shaped by the mainstream media coverage of high-profile stories. A shift in public attitude towards these issues has encouraged more victims to come forward and share their stories, many detailing harrowing events perpetrated by successful businessmen and politicians. Credible accusations continue to come forward, and while some end in legal action, many do not, and perpetrators face little to no consequences. I examined how individuals respond to issues of sexual misconduct and assault in politics, and based on the severity of the accusation, how they respond. Through my survey research, I was able to isolate responses to see the influence that party affiliation and gender have on individuals’ opinion formation, as well as how the politicians’ prior conduct history affects the response. The research reflects an overall partisan difference between Republicans and Democrats in terms of reaction, with Democrats consistently being in favor of harsher consequences, both when Democratic and Republican perpetrators are involved. Additionally, individuals respond more harshly when the accused politician is of the opposite party. This pattern was consistent across the different severity treatments, as well as the conduct history treatments. Generally, when a history of sexual misconduct was present, individuals of both parties reacted more harshly. In summation, there is significant evidence of partisan bias in the public evaluation of sexual misconduct allegations. I also discuss several prominent cases of sexual misconduct by politicians in order to demonstrate how the public has formed opinions in the wake of scandal. This research has both policy and scholarly implications that will be of great value now and into the future as public response to allegations of sexual misconduct continues to evolve

Believe It or Not: Adding Belief Annotations to Databases

We propose a database model that allows users to annotate data with belief statements. Our motivation comes from scientific database applications where a community of users is working together to assemble, revise, and curate a shared data repository. As the community accumulates knowledge and the database content evolves over time, it may contain conflicting information and members can disagree on the information it should store. For example, Alice may believe that a tuple should be in the database, whereas Bob disagrees. He may also insert the reason why he thinks Alice believes the tuple should be in the database, and explain what he thinks the correct tuple should be instead. We propose a formal model for Belief Databases that interprets users' annotations as belief statements. These annotations can refer both to the base data and to other annotations. We give a formal semantics based on a fragment of multi-agent epistemic logic and define a query language over belief databases. We then prove a key technical result, stating that every belief database can be encoded as a canonical Kripke structure. We use this structure to describe a relational representation of belief databases, and give an algorithm for translating queries over the belief database into standard relational queries. Finally, we report early experimental results with our prototype implementation on synthetic data.Comment: 17 pages, 10 figure

To believe or not to believe: Assessing the credibility of social media as non-formal learning

Examines the criteria used by adults to assess the credibility of resources encountered online and the extent to which they differ from younger cohorts. Key information for design of adult online education

To Believe or Not Believe? The Untold Story of the Agnostic

My Experience

TO BELIEVE OR NOT TO BELIEVE – INVESTIGATING THE EFFECT OF COMMERCE-ORIENTED MEDIA REVENUE MODELS ON CONTENT CREDIBILITY

Many content providers still struggle to establish viable revenue models on the Internet and thus try to tap new sources of income. Using content to drive sales of related products or services such as in affiliate marketing or content-driven commerce appears to be a promising solution for this issue. However, these commerce-oriented revenue models may cast doubt on the credibility of the content, which is an important success factor for the providers. Drawing upon credibility concepts and information processing theories from communication science, we conducted a vignette-based online experiment to investigate whether content credibility is affected by the provider’s revenue model. Participants in the experiment were shown a screenshot of a website, which was monetized either by advertising, affiliate marketing or content-driven commerce. Our results indicate that content credibility in the affiliate marketing scenario was higher than in the content-driven commerce or the advertising scenario. A mediation analysis revealed that this effect was mediated by the content provider’s trustworthiness. Our findings shed light on the relationship between credibility and monetization of content on the Internet. Moreover, they are helpful for practitioners in the media industry in designing optimal revenue generation strategies

To Believe or not to Believe - That is not the (Only) Question: the Hybrid View of Privacy

In this paper, we defend what we call the ‘Hybrid View’ of privacy. According to this view, an individual has privacy if, and only if, no one else forms an epistemically warranted belief about the individual’s personal matters, nor perceives them. We contrast the Hybrid View with what seems to be the most common view of what it means to access someone’s personal matters, namely the Belief-Based View. We offer a range of examples that demonstrate why the Hybrid View is more plausible than the Belief-Based View. Finally, we show how the Hybrid View generates a more plausible fit between the concept of privacy, and a (morally objectionable) violation of privacy