The role of dehumanization is little explored in the literature of misinformation studies, primarily seen as a mechanism of rightwing authoritarianism (RWA) to denigrate members of targeted outgroups.One noted aspect of dehumanization is the unexpected impact it has on the person enacting it as well as victims, resulting in the noticeable phenomenon of self-dehumanization, in which persons deny their own humanity.Notably, a link to selfdehumanization in information behavior may be evident in the foundational research of Eflreda Chatman, whose examination of information poverty, life in the round and normative behavioral theory resulted in sometimes perplexing findings where those in marginalized groups often would refuse to seek out information helpful to them.It is theorized that a missing link in Chatman's approach, accounting for some of the unexpected results in her research, may be the effect of self-dehumanization on marginalized groups and their members.The result of such self-dehumanization effects may impact information use, pointing toward another factor in the way that misinformation is believed or spread.This paper shows a novel way forward in the field of information science and information behavior, examining an important aspect of how misinformation may come to be believed and shared within smaller worlds and codified in marginalized groups' normative behaviors.Finally, the paper points the way toward a nascent conceptualization of a 'misinformation need' inherent to the normative behaviors of those within marginalized groups.

In this paper, we introduce our project: Cross-campus Interdisciplinary Responsible Computing Learning Experiences (CIRCLE). This project introduced undergraduate students in all fields to ethical computing by integrating responsible computing and ethical Artificial Intelligence (AI) into existing undergraduate courses in computer science major and organizing a series of speaker events at San Jose State University. The project aimed to redesign curricula and pedagogy to include diverse perspectives and interdisciplinarity, foster critical thinking about the design and use of technology, promote cultural sensitivity to recognize how technology may perpetuate or deepen inequality, and create more equitable systems that build responsible ethical tools. Enrollment in a course with modified syllabus focused on ethical AI and Computer Vision increased by 21 more students (increased in enrollment is approximately 123.53%), and attendance for the speaker series rose from 31.4% to 100%• This work demonstrates a case study that provides ways to increase students' participation and enrollment in Computer Science courses and the benefits of expanding experiences to bring responsible computing to a much broader range of students on campus.

Abstract Prior research in human–computer interaction suggests that system‐level clarifications are necessary for understanding user intent and communicating effectively with the user. Such clarifications or explanations could contain the system's abstract knowledge of the search or a functional description of the search process (queries and information sources employed). While these interactions may aid the user and the agent in better understanding each other, very few studies have explored the influence of such clarifications on the users' search experience. This research examines whether and how system‐level clarifications (or explanations) affect the user experience when searching through spoken dialogues. We analyzed user satisfaction and preferences in systems with and without explicit clarifications in a within‐subjects Wizard‐of‐Oz user study. We recruited 25 participants and collected user–system interaction data for 50 search sessions. The user feedback was collected using pre‐ and post‐task surveys and exit interviews. Statistical and qualitative analysis of user responses yielded some interesting findings. While Wilcoxon Signed Rank Test found that using explicit system‐level clarifications had no positive influence on the user's search experience, the overall search experience degraded with system clarifications (Z = −2.066, p = 0.04). The user interview data provided valuable insights into how and when clarifications should be offered to the user.

Understanding human spoken dialogues in an information-seeking scenario is a significant challenge for IR researchers. Prior literature in intelligent systems suggests that by identifying speech acts in spoken dialogues, we can identify the search intent and the information needs of the user. Therefore, in this paper, we have used speech acts to address the problem of natural language understanding in conversational search systems. First, we collected human-system interaction data through a Wizard-of-Oz study. Next, we developed a gold-standard dataset where the human-system conversations are labeled with corresponding speech acts. Finally, we built the Multi-channel Deep Attention Network (MDAN) to identify the speech acts in information-seeking dialogues. The results highlight that the best performing model predicts speech acts with 90.2% accuracy. The MDAN architecture outperforms not only all traditional machine learning models but also the state-of-the-art single-channel BERT by 3.3 absolute points. We performed ablation analysis to show the impact of the three channels of MDAN individually and in combination. The results indicate that the best performance is achieved using all three channels for speech act prediction.

Recent researches in conversational IR have explored problems related to context enhancement, question-answering, and query reformulations. However, very few researches have focused on result presentation over audio channels. The linear and transient nature of speech makes it cognitively challenging for the user to process a large amount of information. Presenting the search results (from SERP) is equally challenging as it is not feasible to read out the list of results. In this paper, we propose a study to evaluate the users' preference of modalities when using conversational search systems. The study will help us to understand how results should be presented in a conversational search system. As we observe how users search using audio queries, interact with the intermediary, and process the results presented, we aim to develop an insight on how to present results more efficiently in a conversational search setting. We also plan on exploring the effectiveness and consistency of different media in a conversational search setting. Our observations will inform future designs and help to create a better understanding of such systems.

In this research, we investigate the behavioral patterns exhibited in different search sessions as users attempt to complete search tasks of increasing cognitive complexity. The search tasks, which are exploratory in nature, have been designed using the Taxonomy of Educational Objectives, and are presented to the users hierarchically. We capture naturalistic search behavior of the users in real world (non-lab) setting using a Chrome browser plugin. The research analyzes the web log data of the users to assess if and how the web search behavior of the users changes over different search sessions. We also look at the different demographic factors like age and gender, educational factors like the academic background, read and write proficiency in English, and search skills to determine if these factors influence the web search behavior of the users. Our results indicate that search sessions have significant effects on the web search behavior of the users. Most of the web search behaviors differed significantly across search sessions. Of the secondary factors, gender showed significant effect on the query reformulations (measured using average edit distance between queries) and query length (measured using number of words per query) while year of study affected only the average query length. Search experience had significant effect on all the web search behaviors.

Recent developments in conversational IR have raised questions about the nature of interactions which occur between the user and the system and the cognitive capabilities expected of such systems. In our research, we investigate the completeness of existing theoretical frameworks in explaining conversational search data propose modifications to such systems. The linear and transient nature of speech makes it cognitively challenging for the user to process a large amount of information. We propose a study to evaluate the users' preference of modalities when using conversational search systems. The study will help us to understand how results should be presented in a conversational search system. As we observe how users search using audio queries, interact with the intermediary, and process the results presented, we aim to develop an insight on how to present results more efficiently in a conversational search setting. We also plan on exploring the effectiveness and consistency of different media in a conversational search setting. Our observations will inform future designs and help to create a better understanding of such systems.

In this paper, we investigate the relationship between searching and learning, by conceptualizing information seeking as a learning process, and learning as an outcome of the information seeking process. We present the participants with four search tasks, each of them designed to represent different cognitive levels of learning. Through quantitative analysis of the participants» Web search logs, we examine how individual search behavior is influenced by different task complexity levels as we present the tasks in a hierarchical order. We also explore how the perceived learning outcomes and processes, and the different learning actions, are related to the levels of cognitive complexity. By analyzing the search logs, self-reports, interview data, and the reports, both quantitatively and qualitatively, we infer that searching and learning are not isolated but co-existing processes. Distinct search patterns and learning outcomes were observed in tasks of different cognitive complexities, and overlapping learning actions were observed for the different tasks.

In an information seeking episode, users often look for sources in online and offline environments depending on the task at hand. However, at most times users consider factors such as ease, time taken to complete the task, and the number of sources to be consulted as the essential factors while fulfilling the information seeking task. In our study, we explore the role of different cost variables -- ease, time taken to complete the task, and the number of sources consulted -- as the factors to be explored based on different cognitive task complexity levels, from Bloom»s taxonomy, by conducting a user study. We study the different search behaviors shown by users in online and offline environments based on the different cognitive task complexity levels and the three cost variables. We observed intriguing results that show factors such as ease, time, and the number of sources play a role in source selection while completing the tasks. Our study is a novel proposition in that we explore research in the direction of source selection based on different cognitive task complexity levels. The findings will contribute to shaping how tasks should be designed to use sources in a helpful and convenient manner. Moreover, the results also advance our understanding of the role that different affordances play in online and offline search behavior.