Computer-Mediated Deception: Strategies Revealed by Language-Action Cues in Spontaneous Communication

Strategic deception in call centers: impacts on well-being, cognition, and work motivation

The literature indicated that deceivers in face-to-face communication experience psychological strains derived from guilt or distress associated with violating conversational rules. We proposed that this also applies to telephone-mediated deception. Drawing insights from the theoretical and empirical literature, we surmised that strategic trickery utilized by outsourced call center agents would elicit adverse psychological reactions that have unfavorable impacts on their well-being, cognition, and work motivation. We used structural equation modeling to test our hypotheses using data from a sample of 554 outsourced Filipino call service agents who worked graveyard shifts to cater to mainly American customers. The results suggested that strategic deception increases the experience of cognitive dissonance while negatively impacting psychological well-being and intrinsic work motivation. The results also showed that dissonance negatively influences well-being and intrinsic motivation and partially mediates the deception-motivation relationship. Unlike previous findings, however, our multivariate analyses revealed that well-being and motivation were not correlated. Our original findings have theoretical and practical implications.

Sorting Insiders From Co-Workers: Remote Synchronous Computer-Mediated Triage for Investigating Insider Attacks

OBJECTIVE

Develop and investigate the potential of a remote, computer-mediated and synchronous text-based triage, which we refer to as InSort, for quickly highlighting persons of interest after an insider attack.

BACKGROUND

Insiders maliciously exploit legitimate access to impair the confidentiality and integrity of organizations. The globalisation of organisations and advancement of information technology means employees are often dispersed across national and international sites, working around the clock, often remotely. Hence, investigating insider attacks is challenging. However, the cognitive demands associated with masking insider activity offer opportunities. Drawing on cognitive approaches to deception and understanding of deception-conveying features in textual responses, we developed InSort, a remote computer-mediated triage.

METHOD

During a 6-hour immersive simulation, participants worked in teams, examining password protected, security sensitive databases and exchanging information during an organized crime investigation. Twenty-five percent were covertly incentivized to act as an 'insider' by providing information to a provocateur.

RESULTS

Responses to InSort questioning revealed insiders took longer to answer investigation relevant questions, provided impoverished responses, and their answers were less consistent with known evidence about their behaviours than co-workers.

CONCLUSION

Findings demonstrate InSort has potential to expedite information gathering and investigative processes following an insider attack.

APPLICATION

InSort is appropriate for application by non-specialist investigators and can be quickly altered as a function of both environment and event. InSort offers a clearly defined, well specified, approach for use across insider incidents, and highlights the potential of technology for supporting complex time critical investigations.

A Hashtag Is Worth a Thousand Words: An Empirical Investigation of Social Media Strategies in Trademarking Hashtags

Firms of all sizes are “joining the conversation” on social media platforms and increasingly trademarking hashtags related to their products and brands. This added effort to protect intellectual property and its impact on social media engagement have not been investigated in the literature. In this study, we find that trademarking hashtags plays a pivotal role in increasing social media audience engagement and information dissemination. More importantly, this positive effect is stronger for firms with fewer Twitter followers. Digging deeper into the underlying mechanisms, we find that trademarking hashtags makes composing tweets with certain linguistic styles more critical: It can amplify the positive effects of trademarking hashtags on social media audience engagement. Our findings highlight important managerial implications of trademarking hashtags. First of all, we examine whether trademarking a hashtag helps or hurts a firm in terms of its social media audience engagement. Further, we show, to maximize the effectiveness of trademarking hashtags, how firms should develop the right social media engagement strategies by taking specific communication and linguistic styles into account. Our results provide useful insights to firms in understanding the key benefits of signaling through trademarking hashtags on social media engagement.

Verbal vs. Nonverbal Cues in Static and Dynamic Contexts of Fraud Detection in Crowdsourcing

As an important mode of open innovation, crowdsourcing can effectively integrate external resources, enabling enterprises to obtain stronger competitiveness and more benefits at a faster speed and lower cost. However, this mode has inevitable intellectual property protection challenges, especially on contest-based crowdsourcing platforms. Previous studies mostly focused on the protection of the rights of sponsors while ignoring the rights of workers, rarely paying attention to sponsor fraud, which may reduce the enthusiasm of participants and eventually turn crowdsourcing;' into a lemon market. This study proposes several fraud detection models to address this problem on contest-based crowdsourcing platforms. Furthermore, this paper explores and compares the value of four types of information as deception cues in crowdsourcing contexts via data mining technology and machine learning methods. The results benefit participants in crowdsourcing markets and contribute to fraud detection research and open innovation in the knowledge economy.

A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting

Language use differs between truthful and deceptive statements, but not all differences are consistent across people and contexts, complicating the identification of deceit in individuals. By relying on fact-checked tweets, we showed in three studies (Study 1: 469 tweets; Study 2: 484 tweets; Study 3: 24 models) how well personalized linguistic deception detection performs by developing the first deception model tailored to an individual: the 45th U.S. president. First, we found substantial linguistic differences between factually correct and factually incorrect tweets. We developed a quantitative model and achieved 73% overall accuracy. Second, we tested out-of-sample prediction and achieved 74% overall accuracy. Third, we compared our personalized model with linguistic models previously reported in the literature. Our model outperformed existing models by 5 percentage points, demonstrating the added value of personalized linguistic analysis in real-world settings. Our results indicate that factually incorrect tweets by the U.S. president are not random mistakes of the sender.

The anatomy of ‘fake news’: Studying false messages as digital objects

Public concern about ‘fake news’ skyrocketed following the 2016 US presidential election and the Brexit referendum, and has only intensified since then. A burgeoning body of research on the topic is emerging, and conceptual clarity is vital for this research to converge into a cumulative body of knowledge; the purpose of this article is to underline and address some of the conceptual clutter and ambiguities around the concept of fake news and situate it within its social context. To do so, we first discuss the problems with current terminology and conceptualisation, and then draw on recent developments on the ontology of digital objects and their attributes to shift the focus from fake news to false messages, a type of syntactic digital objects comprised of content and structure and characterised by attributes of editability, openness, interactivity, and distributedness. Then we expand this concept further by placing it within a network of actors and digital objects. Our analysis uncovers several areas of research that have been overlooked in the study of fake news.

Longitudinal Effects of Mediums of Word Explanation on L2 Vocabulary Learning Strategies Among Chinese Grade-7 Students

This longitudinal study investigated how different mediums of word explanation affected the use of English vocabulary strategies among Chinese Grade-7 students. 170 students were tested on their English receptive vocabulary size and vocabulary strategy application before and after an 8.33-month intervention. Students were divided into three experimental groups and one control group. The three experimental groups were provided with learning materials that explained the target vocabulary in three mediums, respectively: English-only, English-and-Chinese, and Chinese-only. Results showed that, after the intervention, receptive vocabulary size did not have any direct significant impact on vocabulary strategy development, whereas mediums of word explanation materials impacted students' application of vocabulary learning strategies (VLS) in different ways. Our findings showed that the English-only mediums significantly enhanced students' use of metacognition, cognition, and memorization strategies, but decreased social strategy development. Chinese-only mediums significantly facilitated cognition and memorization strategy development. Implications for L2 vocabulary education are discussed.