Creativity is an essential part of people's daily life and work across a range of everyday tasks. However, little prior work has explored how people use search engines and information resources as part of their creative processes, and how systems might better support users working on creative tasks. In this paper, we conducted an online survey with 175 participants to explore how people use search engines and online resources (e.g., images, videos, and social media) to support their creative tasks. Our participants reported information seeking to support a broad range of everyday creativity including tasks in arts, writing, crafts, and technical projects. Our findings show that participants' tasks included multiple stages of creative processes (e.g., creating ideas, combining ideas, executing plans) and that participants reported using search engines along with other tools (such as images and videos) to facilitate their creative process. By using Bayesian random effects regression models, we found that different stages of the creative process influence participants' use of tools. For example, for tasks that involved creating ideas, participants were more likely to use images and social sites, and when needing to put ideas into practice they were more likely to use videos. We also found differences in users' satisfaction with using the tools for different creative stages. Based on our findings, we provide recommendations for supporting users' information seeking needs during creative tasks.
{"title":"Understanding How People use Search to Support their Everyday Creative Tasks","authors":"Yinglong Zhang, Robert G. Capra","doi":"10.1145/3295750.3298936","DOIUrl":"https://doi.org/10.1145/3295750.3298936","url":null,"abstract":"Creativity is an essential part of people's daily life and work across a range of everyday tasks. However, little prior work has explored how people use search engines and information resources as part of their creative processes, and how systems might better support users working on creative tasks. In this paper, we conducted an online survey with 175 participants to explore how people use search engines and online resources (e.g., images, videos, and social media) to support their creative tasks. Our participants reported information seeking to support a broad range of everyday creativity including tasks in arts, writing, crafts, and technical projects. Our findings show that participants' tasks included multiple stages of creative processes (e.g., creating ideas, combining ideas, executing plans) and that participants reported using search engines along with other tools (such as images and videos) to facilitate their creative process. By using Bayesian random effects regression models, we found that different stages of the creative process influence participants' use of tools. For example, for tasks that involved creating ideas, participants were more likely to use images and social sites, and when needing to put ideas into practice they were more likely to use videos. We also found differences in users' satisfaction with using the tools for different creative stages. Based on our findings, we provide recommendations for supporting users' information seeking needs during creative tasks.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122609228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bahareh Sarrafzadeh, Ahmed Hassan Awadallah, Milad Shokouhi
Despite the centrality of email in the daily routines of knowledge workers, fundamental aspects of its usage are still poorly understood. We are particularly interested in understanding one aspect of email management, email triage, the process of going through unhandled email and deciding what to do with them. In this paper we investigate the email triage behavior by presenting interview and survey results that characterize user behavior and needs. The results highlight current challenges and enhance our understanding of how the triage process can be more effectively supported.
{"title":"Exploring Email Triage: Challenges and Opportunities","authors":"Bahareh Sarrafzadeh, Ahmed Hassan Awadallah, Milad Shokouhi","doi":"10.1145/3295750.3298960","DOIUrl":"https://doi.org/10.1145/3295750.3298960","url":null,"abstract":"Despite the centrality of email in the daily routines of knowledge workers, fundamental aspects of its usage are still poorly understood. We are particularly interested in understanding one aspect of email management, email triage, the process of going through unhandled email and deciding what to do with them. In this paper we investigate the email triage behavior by presenting interview and survey results that characterize user behavior and needs. The results highlight current challenges and enhance our understanding of how the triage process can be more effectively supported.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"10 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116586145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this study was to find out what is historians' task-based information interaction like in digital environments. The study was based on TBII framework [20]. The research was conducted in real-life setting using interview and shadowing. The following task process types were identified: (i) searching secondary sources, (ii) reading and making notes, (iii) collecting and processing data, (iv) analyzing and (v) writing. We found that IR interfaces were not always optimal for historians' use, and that the interfaces should support the expression of information needs better. The data processing consumes a lot of historians' time and they could benefit from designing better tools. We also found out that task process types have an effect on how digital tools or data are being used. Therefore, the context in which the tool or data is being used, should be taken into account when designing tools for historians.
{"title":"Interacting with Digital Documents: A Real Life Study of Historians' Task Processes, Actions and Goals","authors":"Laura Korkeamäki, S. Kumpulainen","doi":"10.1145/3295750.3298931","DOIUrl":"https://doi.org/10.1145/3295750.3298931","url":null,"abstract":"The aim of this study was to find out what is historians' task-based information interaction like in digital environments. The study was based on TBII framework [20]. The research was conducted in real-life setting using interview and shadowing. The following task process types were identified: (i) searching secondary sources, (ii) reading and making notes, (iii) collecting and processing data, (iv) analyzing and (v) writing. We found that IR interfaces were not always optimal for historians' use, and that the interfaces should support the expression of information needs better. The data processing consumes a lot of historians' time and they could benefit from designing better tools. We also found out that task process types have an effect on how digital tools or data are being used. Therefore, the context in which the tool or data is being used, should be taken into account when designing tools for historians.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128066113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geo-temporal visualization of Twitter search results is a challenging task since the simultaneous display of all matching tweets would result in a saturated and unreadable display. In such settings, clustering search results can assist users to scan only a few coherent groups of related tweets rather than many individual tweets. However, in practice, the use of unsupervised clustering methods such as K -Means does not necessarily guarantee that the clusters themselves are relevant. Therefore, we develop a novel method of relevance-driven clustering for visual information retrieval to supply users with highly relevant clusters representing different information perspectives of their queries. We specifically propose a Visual Twitter Information Retrieval (Viz-TIR) tool for relevance-driven clustering and ranking of Twitter search results. At the heart of Viz-TIR is a fast greedy algorithm that optimizes an approximation of an expected F1-Score metric to generate these clusters. We demonstrate its effectiveness w.r.t. K -Means and a baseline method that shows all top matching results on a scenario related to searching natural disasters in US-based Twitter data spanning 2013 and 2014. Our demo shows that Viz-TIR is easy to use and more precise in extracting geo-temporally coherent clusters given search queries in comparison to K-Means, thus aiding the user in visually searching and browsing social network content. Overall, we believe this work enables new opportunities for the synthesis of information retrieval as well as combined relevance and display-aware optimization techniques to support query-adaptive visual information exploration interfaces.
Twitter搜索结果的地理时间可视化是一项具有挑战性的任务，因为同时显示所有匹配的tweet将导致饱和且不可读的显示。在这种设置中，聚类搜索结果可以帮助用户只扫描几组连贯的相关tweet，而不是许多单独的tweet。然而，在实践中，使用K -Means等无监督聚类方法并不一定保证聚类本身是相关的。因此，我们开发了一种新的关联驱动聚类方法用于视觉信息检索，为用户提供代表其查询的不同信息视角的高度相关聚类。我们特别提出了一个可视化Twitter信息检索(Viz-TIR)工具，用于Twitter搜索结果的相关性驱动聚类和排名。Viz-TIR的核心是一个快速贪婪算法，它优化了预期F1-Score指标的近似值来生成这些聚类。我们展示了它的有效性w.r.t. K -Means和一个基线方法，该方法显示了在2013年和2014年美国Twitter数据中搜索自然灾害相关场景的所有顶级匹配结果。我们的演示表明，与K-Means相比，Viz-TIR易于使用，并且在提取给定搜索查询的地理时间相干簇方面更精确，从而帮助用户在视觉上搜索和浏览社交网络内容。总的来说，我们相信这项工作为综合信息检索以及结合相关性和显示感知优化技术提供了新的机会，以支持查询自适应的视觉信息探索界面。
{"title":"Relevance-driven Clustering for Visual Information Retrieval on Twitter","authors":"Mohamed Reda Bouadjenek, S. Sanner","doi":"10.1145/3295750.3298914","DOIUrl":"https://doi.org/10.1145/3295750.3298914","url":null,"abstract":"Geo-temporal visualization of Twitter search results is a challenging task since the simultaneous display of all matching tweets would result in a saturated and unreadable display. In such settings, clustering search results can assist users to scan only a few coherent groups of related tweets rather than many individual tweets. However, in practice, the use of unsupervised clustering methods such as K -Means does not necessarily guarantee that the clusters themselves are relevant. Therefore, we develop a novel method of relevance-driven clustering for visual information retrieval to supply users with highly relevant clusters representing different information perspectives of their queries. We specifically propose a Visual Twitter Information Retrieval (Viz-TIR) tool for relevance-driven clustering and ranking of Twitter search results. At the heart of Viz-TIR is a fast greedy algorithm that optimizes an approximation of an expected F1-Score metric to generate these clusters. We demonstrate its effectiveness w.r.t. K -Means and a baseline method that shows all top matching results on a scenario related to searching natural disasters in US-based Twitter data spanning 2013 and 2014. Our demo shows that Viz-TIR is easy to use and more precise in extracting geo-temporally coherent clusters given search queries in comparison to K-Means, thus aiding the user in visually searching and browsing social network content. Overall, we believe this work enables new opportunities for the synthesis of information retrieval as well as combined relevance and display-aware optimization techniques to support query-adaptive visual information exploration interfaces.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Searching for images posted within social media services such as Twitter relies on matching textual queries to the contents of the posts that include the images. Unfortunately, social media posts may not always provide accurate or meaningful descriptions of the contents of the embedded images, making searching for images a challenging task. In this research, we augment the textual contents of the posts with new information extracted from the images using image processing and deep learning methods, and provide a visual interface to enable interactive image retrieval. A user study was conducted with 28 participants to collect evidence on how our approach was used in relation to Vakkari's three-stage model of information seeking. We also analyzed participants' perceptions of usefulness, ease of use, and satisfaction in comparison to a common grid-based image search interface. The results from this study highlight the value of providing visual and interactive features to enable searchers to discover images from social media sources.
{"title":"An Interactive Image Retrieval Approach to Searching for Images on Social Media","authors":"Manali Gaikwad, O. Hoeber","doi":"10.1145/3295750.3298930","DOIUrl":"https://doi.org/10.1145/3295750.3298930","url":null,"abstract":"Searching for images posted within social media services such as Twitter relies on matching textual queries to the contents of the posts that include the images. Unfortunately, social media posts may not always provide accurate or meaningful descriptions of the contents of the embedded images, making searching for images a challenging task. In this research, we augment the textual contents of the posts with new information extracted from the images using image processing and deep learning methods, and provide a visual interface to enable interactive image retrieval. A user study was conducted with 28 participants to collect evidence on how our approach was used in relation to Vakkari's three-stage model of information seeking. We also analyzed participants' perceptions of usefulness, ease of use, and satisfaction in comparison to a common grid-based image search interface. The results from this study highlight the value of providing visual and interactive features to enable searchers to discover images from social media sources.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123986449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of the existing IR studies employed final values of search behavior measures in building evaluation metrics. However, according to the theories and empirical evidences from Behavioral Economics studies, in people's evaluations of actions and outcomes, the carriers of the values of different actions are gains and losses defined relative to a reference point, rather than the absolute final assets. Based on this idea, I will first explore how users' levels of search satisfaction are affected by the gains and losses defined relative to the pre-search expectations of system performance or reference levels in a controlled lab study. Then, based on the data collected from a field study, I will test the predicative power of my reference-dependent models (built upon delta-value-based behavioral features given the corresponding reference points) in predicting user satisfaction in naturalistic settings, aiming to examine the extent to which the reference-dependent approach can approximate real users' search evaluations. The findings of this work can help us better understand the subjectivity, bias, and variation in users' evaluation of search experience and thus have implications for user modeling and system recommendations design.
{"title":"A Reference-Dependent Model of Search Evaluation","authors":"Jiqun Liu","doi":"10.1145/3295750.3298970","DOIUrl":"https://doi.org/10.1145/3295750.3298970","url":null,"abstract":"Most of the existing IR studies employed final values of search behavior measures in building evaluation metrics. However, according to the theories and empirical evidences from Behavioral Economics studies, in people's evaluations of actions and outcomes, the carriers of the values of different actions are gains and losses defined relative to a reference point, rather than the absolute final assets. Based on this idea, I will first explore how users' levels of search satisfaction are affected by the gains and losses defined relative to the pre-search expectations of system performance or reference levels in a controlled lab study. Then, based on the data collected from a field study, I will test the predicative power of my reference-dependent models (built upon delta-value-based behavioral features given the corresponding reference points) in predicting user satisfaction in naturalistic settings, aiming to examine the extent to which the reference-dependent approach can approximate real users' search evaluations. The findings of this work can help us better understand the subjectivity, bias, and variation in users' evaluation of search experience and thus have implications for user modeling and system recommendations design.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129112986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With a process- and interaction-focus, this study posited that the nature and flow of users' interactions on a social question & answer (SQA) thread can impact their collaborative information seeking outcomes. Interaction Process Analysis (IPA) was applied to investigate the types and outcomes of users' social-emotional interactions. Over 1,000 Stack Overflow postings were manually coded; Chi-square tests and logistic regressions were used for analysis. The study found that over half of the sample included IPA social-emotional acts. Interestingly, Disagrees , an act in IPA's negative social-emotional area, was the most frequently found category. Disagrees exhibited a significant negative relationship with the post-level outcome, post score, but a significant positive main effect and an interaction effect with a thread-level outcome, view count. The study identified two tension points: (1) potential benefits for the group of collaborative information seekers, at the slight expense of the individual who performed the negative social-emotional act; and (2) strains between the instrumental vs. social aspects of SQA. Research and practical implications of the findings were discussed.
{"title":"Take One for the Team: Social-emotional Interactions and Outcomes on Social Question and Answers Sites","authors":"Sei-Ching Joanna Sin, Xinran Chen","doi":"10.1145/3295750.3298955","DOIUrl":"https://doi.org/10.1145/3295750.3298955","url":null,"abstract":"With a process- and interaction-focus, this study posited that the nature and flow of users' interactions on a social question & answer (SQA) thread can impact their collaborative information seeking outcomes. Interaction Process Analysis (IPA) was applied to investigate the types and outcomes of users' social-emotional interactions. Over 1,000 Stack Overflow postings were manually coded; Chi-square tests and logistic regressions were used for analysis. The study found that over half of the sample included IPA social-emotional acts. Interestingly, Disagrees , an act in IPA's negative social-emotional area, was the most frequently found category. Disagrees exhibited a significant negative relationship with the post-level outcome, post score, but a significant positive main effect and an interaction effect with a thread-level outcome, view count. The study identified two tension points: (1) potential benefits for the group of collaborative information seekers, at the slight expense of the individual who performed the negative social-emotional act; and (2) strains between the instrumental vs. social aspects of SQA. Research and practical implications of the findings were discussed.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131047289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research aims to identify the impact of dyslexia on the information-seeking behaviour of undergraduate students within the context of preparation for a HE assignment by understanding the barriers to information-seeking and the workarounds employed. This will allow recommendations for support, through instruction and system design, to be made. A qualitative approach has been adopted to gain a rich, in-depth understanding of how dyslexia impacts information-seeking and naturalistic data has been collected. Participant captured screen recordings with follow up interviews have been collected and retrospective think aloud sessions are ongoing with undergraduate students with dyslexia. Initial findings suggest that dyslexia is causing barriers to information-seeking due to cognitive and affective challenges. Barriers identified from initial findings included difficulties attributed to spelling and memory difficulties which could manifest in the focus formulation phase of Kuhlthau's ISP and Bates' berrypicking model. Affective barriers relating to self-efficacy while information-seeking that could cause the information-seeker frustration were also found. There were indications that workarounds are being employed to try and overcome the difficulties to information-seeking caused by dyslexia, such as the use of query building aids, and the effectiveness of these workarounds for information-seekers with dyslexia will be investigated in future work.
{"title":"The Impact of Dyslexia on the Information-Seeking Behaviour of Undergraduate Students","authors":"Lynne Cole","doi":"10.1145/3295750.3298971","DOIUrl":"https://doi.org/10.1145/3295750.3298971","url":null,"abstract":"This research aims to identify the impact of dyslexia on the information-seeking behaviour of undergraduate students within the context of preparation for a HE assignment by understanding the barriers to information-seeking and the workarounds employed. This will allow recommendations for support, through instruction and system design, to be made. A qualitative approach has been adopted to gain a rich, in-depth understanding of how dyslexia impacts information-seeking and naturalistic data has been collected. Participant captured screen recordings with follow up interviews have been collected and retrospective think aloud sessions are ongoing with undergraduate students with dyslexia. Initial findings suggest that dyslexia is causing barriers to information-seeking due to cognitive and affective challenges. Barriers identified from initial findings included difficulties attributed to spelling and memory difficulties which could manifest in the focus formulation phase of Kuhlthau's ISP and Bates' berrypicking model. Affective barriers relating to self-efficacy while information-seeking that could cause the information-seeker frustration were also found. There were indications that workarounds are being employed to try and overcome the difficulties to information-seeking caused by dyslexia, such as the use of query building aids, and the effectiveness of these workarounds for information-seekers with dyslexia will be investigated in future work.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129659929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowledge workers (such as healthcare information professionals, patent agents and legal researchers) need to create and execute search strategies that are accurate, repeatable and transparent. The traditional solution offered by most database vendors is to use proprietary line-by-line 'query builders'. However, these offer limited support for error checking or query optimisation, and their output can often be compromised by errors and inefficiencies. Using the healthcare domain for context, we demonstrate a new approach to search strategy formulation in which concepts are expressed as objects on a two-dimensional canvas, and relationships are articulated using direct manipulation. This approach eliminates many sources of syntactic error, makes the query semantics more transparent, and offers new ways to optimise, save and share search strategies and best practices
{"title":"A Visual Approach to Query Formulation for Systematic Search","authors":"Tony Russell-Rose, Jon Chamberlain, F. Shokraneh","doi":"10.1145/3295750.3298919","DOIUrl":"https://doi.org/10.1145/3295750.3298919","url":null,"abstract":"Knowledge workers (such as healthcare information professionals, patent agents and legal researchers) need to create and execute search strategies that are accurate, repeatable and transparent. The traditional solution offered by most database vendors is to use proprietary line-by-line 'query builders'. However, these offer limited support for error checking or query optimisation, and their output can often be compromised by errors and inefficiencies. Using the healthcare domain for context, we demonstrate a new approach to search strategy formulation in which concepts are expressed as objects on a two-dimensional canvas, and relationships are articulated using direct manipulation. This approach eliminates many sources of syntactic error, makes the query semantics more transparent, and offers new ways to optimise, save and share search strategies and best practices","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129480047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In public transitional spaces, such as airports, users are faced with diverse challenges regarding information interaction and use. These challenges arise due to the scheduled and/or location-dependent procedures users are required to perform. Understanding what these users need or desire in the context of such spaces, what information is on offer, both online and in situ, and how these aspects interrelate is important to facilitate the design of systems that are accepted by the users concerned. However, very little is known about human information behavior (HIB) in public transitional spaces. As a starting point to understand how behavior in such spaces relates to or differs from information behavior in other contexts, holistically, I will create an explanatory model of airport information behavior by conducting an exploratory grounded theory based field study and relating my findings to those of existing models.
{"title":"Where to Go and What to Do: Towards Understanding Task-Based Information Behavior at Transitional Spaces","authors":"Melanie A. Kilian","doi":"10.1145/3295750.3298972","DOIUrl":"https://doi.org/10.1145/3295750.3298972","url":null,"abstract":"In public transitional spaces, such as airports, users are faced with diverse challenges regarding information interaction and use. These challenges arise due to the scheduled and/or location-dependent procedures users are required to perform. Understanding what these users need or desire in the context of such spaces, what information is on offer, both online and in situ, and how these aspects interrelate is important to facilitate the design of systems that are accepted by the users concerned. However, very little is known about human information behavior (HIB) in public transitional spaces. As a starting point to understand how behavior in such spaces relates to or differs from information behavior in other contexts, holistically, I will create an explanatory model of airport information behavior by conducting an exploratory grounded theory based field study and relating my findings to those of existing models.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125919592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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