Human Information Interaction and the Cognitive Predicting Theory of Trust

Fell, Lauren; Gibson, Andrew; Bruza, Peter; Hoyte, Pamela

doi:10.1145/3343413.3377981

Cited by 7 publications

(11 citation statements)

References 21 publications

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“…Targeting each of these two domains separately is typically the strategy when attempting to study or treat informational phenomena such as fake news or widespread misinformation. However, focusing instead on the context within which these two domains (the person and the information) interact is needed to form a more complete picture [5].…”

Section: Trust In Information and Health Advicementioning

confidence: 99%

“…Given the fact that many governments around the world have exerted greater control over their citizens through the implementation of policies and interventions to control the spread of the virus [1], there exists a significant need to focus on the impact on trust in relationships between governments and their citizens. According to CPTT [5], potential areas of focus include the perceived consistency of decisions made by a government, particularly with regard to an individual's standards, as well as the predictability of other elements within an individual's environment.…”

Section: Trust In Governments and Institutionsmentioning

confidence: 99%

“…Trust is often described in terms of expectations, control and vulnerability (see review [16]). The recent Cognitive Predicting Theory of Trust (CPTT) defines trust as a process of active prediction based on expectations that people form as they interact with their environment [5]. As the forming and testing of predictions about the world is a constant and ongoing process, so too are the many trust decisions individuals make as they go about their daily lives.…”

Section: Introductionmentioning

confidence: 99%

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Trust and COVID-19

Fell

2020

Digit. Gov.: Res. Pract.

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The COVID-19 pandemic raises questions of trust in many domains, including interpersonal relationships, social behaviours, technology, and interactions with institutions and information. This prompts the need to consider trust holistically when attempting to understand it within any of these individual domains. An emerging theory of trust is applied to provide a perspective through which the interrelating effects of trust in multiple contexts can be understood in the wake of the COVID-19 outbreak. Understanding the collective erosion of trust in each of the domains addressed can contribute to understanding how citizens trust in important and reliable health advice provided by governments and institutions. CCS Concepts: • Social and professional topics → User characteristics; • Information systems;

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Section: Trust In Information and Health Advicementioning

confidence: 99%

Section: Trust In Governments and Institutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trust and COVID-19

Fell

2020

Digit. Gov.: Res. Pract.

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“…This option is included to measure trust perception. Trust in the human society depends on credibility [8,17] and previous research on chatbot measures credibility by quesionaire [44]. (b) Discomfort -The response causes emotional discomfort to you.…”

Section: Methodsmentioning

confidence: 99%

“…It suggests that for person A, context from both A and B is important, and the context of B is more important than person A to improve classification. The reason might be that the behavior of person B, could cause distrust or in contrast positive emotion that is highly related to human decision-making [17], thus influencing the behavior of person A. For instance, person A said something non-malevolent, but when person B starts the malevolent sentence, person A would also return malevolent content.…”

Section: Further Analysismentioning

confidence: 99%

Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology

Zhang

Ren

Rijke

2020

Preprint

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Conversational interfaces are increasingly popular as a way of connecting people to information. Corpus-based conversational interfaces are able to generate more diverse and natural responses than template-based or retrieval-based agents. With their increased generative capacity of corpus-based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of recognizing and classifying inappropriate content are mostly focused on a certain category of malevolence or on single sentences instead of an entire dialogue. In this paper, we define the task of Malevolent Dialogue Response Detection and Classification (MDRDC). We make three contributions to advance research on this task. First, we present a Hierarchical Malevolent Dialogue Taxonomy (HMDT). Second, we create a labelled multi-turn dialogue dataset and formulate the MDRDC task as a hierarchical classification task over this taxonomy. Third, we apply state-of-the-art text classification methods to the MDRDC task and report on extensive experiments aimed at assessing the performance of these approaches.

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A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses

Zhang

Ren

Rijke

2021

Asso for Info Science & Tech

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Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus‐based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state‐of‐the‐art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.

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Human Information Interaction and the Cognitive Predicting Theory of Trust

Cited by 7 publications

References 21 publications

Trust and COVID-19

Trust and COVID-19

Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology

A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses

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