Reputation management

Reputation management, originally a public relations term, refers to the influencing, controlling, enhancing, or concealing of an individual's or group's reputation. The growth of the internet and social media led to growth of reputation management companies, with search results as a core part of a client's reputation. Online reputation management, sometimes abbreviated as ORM, focuses on the management of product and service search engine results. Ethical grey areas include mug shot removal sites, astroturfing customer review sites, censoring complaints, and using search engine optimization tactics to influence results. In other cases, the ethical lines are clear; some reputation management companies are closely connected to websites that publish unverified and libelous statements about people. Such unethical companies charge thousands of dollars to remove these posts – temporarily – from their websites. This field of public relations has developed extensively, with the growth of the internet and social media the advent of reputation management companies. The overall outlook of search results has become an integral part of what defines "reputation" and reputation management now exists under two spheres: online and offline reputation management. Online reputation management focuses on the management of product and service search results within the digital space, that is why it is common to see the same suggested links in the first page of a Google search. A variety of electronic markets and online communities like eBay, Amazon and Alibaba have ORM systems built in, and using effective control nodes these can minimize the threat and protect systems from possible misuses and abuses by malicious nodes in decentralized overlay networks. Offline reputation management shapes public perception of a said entity outside the digital sphere using select clearly defined controls and measures towards a desired result ideally representing what stakeholders think and feel about that entity.

Tackling Peer-to-Peer Discrimination in the Sharing Economy

Boi Faltings, Naman Goel, Maxime Rutagarama

Sharing economy platforms such as Airbnb and Uber face a major challenge in the form of peer-to-peer discrimination based on sensitive personal attributes such as race and gender. As shown by a recent study under controlled settings, reputation systems can ...

ACM2020

Disinformation from the Inside: Combining Machine Learning and Journalism to Investigate Sockpuppet Campaigns

Rebekah Jean Overdorf

This paper brings together machine learning and investigative journalism to examine sockpuppets accounts, a historical breed of fake accounts that are non-automated and human-controlled. Due to their flexible and human-centered nature, sockpuppets pose a c ...

ASSOC COMPUTING MACHINERY2020

Construct Validity of Unobtrusive Measures of Organizational Ethical Climates

Tackling Peer-to-Peer Discrimination in the Sharing Economy

Disinformation from the Inside: Combining Machine Learning and Journalism to Investigate Sockpuppet Campaigns

Tackling Peer-to-Peer Discrimination in the Sharing Economy

Construct Validity of Unobtrusive Measures of Organizational Ethical Climates

Disinformation from the Inside: Combining Machine Learning and Journalism to Investigate Sockpuppet Campaigns