Social Media’s impact on Intellectual Property Rights

This is a draft chapter. The final version is available in Handbook of Research on Counterfeiting and Illicit Trade, edited by Peggy E. Chaudhry, published in 2017 by Edward Elgar Publishing Ltd, https://doi.org/10.4337/9781785366451. This material is for private use only, and cannot be used for any other purpose without further permission of the publisher.Peer reviewe

Costly hide and seek pays: Unexpected consequences of deceit in a social dilemma

Deliberate deceptiveness intended to gain an advantage is commonplace in human and animal societies. In a social dilemma, an individual may only pretend to be a cooperator to elicit cooperation from others, while in reality he is a defector. With this as motivation, we study a simple variant of the evolutionary prisoner's dilemma game entailing deceitful defectors and conditional cooperators that lifts the veil on the impact of such two-faced behavior. Defectors are able to hide their true intentions at a personal cost, while conditional cooperators are probabilistically successful at identifying defectors and act accordingly. By focusing on the evolutionary outcomes in structured populations, we observe a number of unexpected and counterintuitive phenomena. We show that deceitful behavior may fare better if it is costly, and that a higher success rate of identifying defectors does not necessarily favor cooperative behavior. These results are rooted in the spontaneous emergence of cycling dominance and spatial patterns that give rise to fascinating phase transitions, which in turn reveal the hidden complexity behind the evolution of deception.Comment: 16 pages, 8 figures; accepted for publication in New Journal of Physic

The Use of Marketing Knowledge in Formulating and Enforcing Consumer Protection Policy

The purpose of this first chapter of the handbook is to discuss how the findings and approaches offered by the marketing discipline are used in consumer protection policy

Online Deception Detection Refueled by Real World Data Collection

The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.Comment: 10 pages, Accepted to Recent Advances in Natural Language Processing (RANLP) 201

A New Role for Human Resource Managers: Social Engineering Defense

[Excerpt] The general risk of social engineering attacks to organizations has increased with the rise of digital computing and communications, while for an attacker the risk has decreased. In order to counter the increased risk, organizations should recognize that human resources (HR) professionals have just as much responsibility and capability in preventing this risk as information technology (IT) professionals. Part I of this paper begins by defining social engineering in context and with a brief history pre-digital age attacks. It concludes by showing the intersection of HR and IT through examples of operational attack vectors. In part II, the discussion moves to a series of measures that can be taken to help prevent social engineering attacks

An Army of Me: Sockpuppets in Online Discussion Communities

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017