Human activity is one of the most important pieces of context affecting an individual's information needs. Understanding the relationship between activities, time, location, and other contextual features can improve the quality of various intelligent systems, including contextual search engines, task managers, digital personal assistants, chat bots, and recommender systems. In this work, we propose a method for extraction of an extensive set of open-vocabulary activities from social media. In particular, we derive tens of thousands of ongoing activities from Twitter, where people share information about their past, present, and future events and, using attached metadata, we establish spatiotemporal models of these activities at the time of posting. While public Twitter content is subject to self-censorship (not all activities are tweeted about), we compare extracted data with unbiased survey data (ATUS) and show evidence that for activities which are tweeted about, the underlying spatiotemporal profiles correctly capture their real distributions of activity conditioned on time and location. Next, to better understand the set of activities present in this dataset (and what role self-censorship may play), we perform a qualitative analysis to understand the activities, locations, and their temporal properties. Finally, we go on to solve predictive tasks centered on the relationship between activity and spatiotemporal context that are aimed at supporting an individual's information needs. Our predictive models, which incorporate text, personal history and temporal features, show a significant performance gain over a strong frequency-based baseline.
{"title":"Understanding Context for Tasks and Activities","authors":"Jan R. Benetka, John Krumm, Paul N. Bennett","doi":"10.1145/3295750.3298929","DOIUrl":"https://doi.org/10.1145/3295750.3298929","url":null,"abstract":"Human activity is one of the most important pieces of context affecting an individual's information needs. Understanding the relationship between activities, time, location, and other contextual features can improve the quality of various intelligent systems, including contextual search engines, task managers, digital personal assistants, chat bots, and recommender systems. In this work, we propose a method for extraction of an extensive set of open-vocabulary activities from social media. In particular, we derive tens of thousands of ongoing activities from Twitter, where people share information about their past, present, and future events and, using attached metadata, we establish spatiotemporal models of these activities at the time of posting. While public Twitter content is subject to self-censorship (not all activities are tweeted about), we compare extracted data with unbiased survey data (ATUS) and show evidence that for activities which are tweeted about, the underlying spatiotemporal profiles correctly capture their real distributions of activity conditioned on time and location. Next, to better understand the set of activities present in this dataset (and what role self-censorship may play), we perform a qualitative analysis to understand the activities, locations, and their temporal properties. Finally, we go on to solve predictive tasks centered on the relationship between activity and spatiotemporal context that are aimed at supporting an individual's information needs. Our predictive models, which incorporate text, personal history and temporal features, show a significant performance gain over a strong frequency-based baseline.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"13 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":"114291920","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}
Chien-yu Huang, Arlene Casey, D. Glowacka, A. Medlar
Scientific literature search engines typically index abstracts instead of the full-text of publications. The expectation is that the abstract provides a comprehensive summary of the article, enumerating key points for the reader to assess whether their information needs could be satisfied by reading the full-text. Furthermore, from a practical standpoint, obtaining the full-text is more complicated due to licensing issues, in the case of commercial publishers, and resource limitations of public repositories and pre-print servers. In this article, we use topic modelling to represent content in abstracts and full-text articles. Using Computer Science as a case study, we demonstrate that how well the abstract summarises the full-text is subfield-dependent. Indeed, we show that abstract representativeness has a direct impact on retrieval performance, with poorer abstracts leading to degraded performance. Finally, we present evidence that how well an abstract represents the full-text of an article is not random, but is a consequence of style and writing conventions in different subdisciplines and can be used to infer an "evolutionary" tree of subfields within Computer Science.
{"title":"Holes in the Outline: Subject-dependent Abstract Quality and its Implications for Scientific Literature Search","authors":"Chien-yu Huang, Arlene Casey, D. Glowacka, A. Medlar","doi":"10.1145/3295750.3298953","DOIUrl":"https://doi.org/10.1145/3295750.3298953","url":null,"abstract":"Scientific literature search engines typically index abstracts instead of the full-text of publications. The expectation is that the abstract provides a comprehensive summary of the article, enumerating key points for the reader to assess whether their information needs could be satisfied by reading the full-text. Furthermore, from a practical standpoint, obtaining the full-text is more complicated due to licensing issues, in the case of commercial publishers, and resource limitations of public repositories and pre-print servers. In this article, we use topic modelling to represent content in abstracts and full-text articles. Using Computer Science as a case study, we demonstrate that how well the abstract summarises the full-text is subfield-dependent. Indeed, we show that abstract representativeness has a direct impact on retrieval performance, with poorer abstracts leading to degraded performance. Finally, we present evidence that how well an abstract represents the full-text of an article is not random, but is a consequence of style and writing conventions in different subdisciplines and can be used to infer an \"evolutionary\" tree of subfields within Computer Science.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"42 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":"133353956","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}
As understanding of web search behavior grows, researchers rapidly develop new study designs to capture and understand search behavior. Researchers have restricted time in which to design a study, develop a collection tool, collect data, analyze it, and report new insights. In particular, sufficient time and development skills are often required to create a tool that meets the needs of any particular web search behavior study. Coagmento is a tool that is developed for facilitating many of the needs for designing and running a lab study, from executing a session flow to collecting log data. By streamlining the programming of unique parts for a specific study, Coagmento helps researchers tailor various parts of running a user study, lowering the barrier for designing and conducting lab study experiments. One-click interactions with a graphical user interface permit researchers to operate through a web-based administrative service to generate stages, search tasks, and questionnaires for their interactive information retrieval studies. In this demonstration, Coagmento provides a solution to increase efficiency in the production of laboratory experiments for web search behavior.
{"title":"Coagmento v3.0: Rapid Prototyping of Web Search Experiments","authors":"Diana Soltani, M. Mitsui, C. Shah","doi":"10.1145/3295750.3298917","DOIUrl":"https://doi.org/10.1145/3295750.3298917","url":null,"abstract":"As understanding of web search behavior grows, researchers rapidly develop new study designs to capture and understand search behavior. Researchers have restricted time in which to design a study, develop a collection tool, collect data, analyze it, and report new insights. In particular, sufficient time and development skills are often required to create a tool that meets the needs of any particular web search behavior study. Coagmento is a tool that is developed for facilitating many of the needs for designing and running a lab study, from executing a session flow to collecting log data. By streamlining the programming of unique parts for a specific study, Coagmento helps researchers tailor various parts of running a user study, lowering the barrier for designing and conducting lab study experiments. One-click interactions with a graphical user interface permit researchers to operate through a web-based administrative service to generate stages, search tasks, and questionnaires for their interactive information retrieval studies. In this demonstration, Coagmento provides a solution to increase efficiency in the production of laboratory experiments for web search behavior.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"1 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":"129256583","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 this perspectives paper, I discuss meaning-making as an information seeking and interaction enterprise. I present meaning-making as a vital human reaction to significant life changes and present indicative evidence of how people go about gathering information for making meaning within their lives. I discuss some of the various forms of information that can be used for meaning-making, why it is an information seeking task that is different to those we are used to in information seeking research, and motivate meaning-making as a new focus for information seeking and information interactions research.
{"title":"Making Meaning: A Focus for Information Interactions Research","authors":"I. Ruthven","doi":"10.1145/3295750.3298938","DOIUrl":"https://doi.org/10.1145/3295750.3298938","url":null,"abstract":"In this perspectives paper, I discuss meaning-making as an information seeking and interaction enterprise. I present meaning-making as a vital human reaction to significant life changes and present indicative evidence of how people go about gathering information for making meaning within their lives. I discuss some of the various forms of information that can be used for meaning-making, why it is an information seeking task that is different to those we are used to in information seeking research, and motivate meaning-making as a new focus for information seeking and information interactions research.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"72 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":"131826770","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}
Interactive information-seeking systems are designed to help users with their struggling during the searching for complex fact checking tasks, where a searcher may have clear information needs but experience difficulty in collecting required information. However, evaluation and comparison of such systems requires a large number of such tasks, which are difficult to collect or make up. To the best of our knowledge, there has not been a commonly used task set for evaluating struggling search of this kind. This paper proposes a convenient method to generate complex fact checking tasks. Each of the generated task has a clearly defined goal, which however takes an average searcher a significant amount of effort to reach. We conducted lab user studies to verify the feasibility of this method. The results confirmed its feasibility and efficiency.
{"title":"Generating Tasks for Study of Struggling Search","authors":"Luyan Xu, Xuan Zhou","doi":"10.1145/3295750.3298949","DOIUrl":"https://doi.org/10.1145/3295750.3298949","url":null,"abstract":"Interactive information-seeking systems are designed to help users with their struggling during the searching for complex fact checking tasks, where a searcher may have clear information needs but experience difficulty in collecting required information. However, evaluation and comparison of such systems requires a large number of such tasks, which are difficult to collect or make up. To the best of our knowledge, there has not been a commonly used task set for evaluating struggling search of this kind. This paper proposes a convenient method to generate complex fact checking tasks. Each of the generated task has a clearly defined goal, which however takes an average searcher a significant amount of effort to reach. We conducted lab user studies to verify the feasibility of this method. The results confirmed its feasibility and efficiency.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"15 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":"129914105","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}
Personal interactions and information access are happening more and more through the mediation of computing devices of various types all around us. In our daily life we use many computing devices running different versions of the same application such as email clients or social media platforms, which alert users about a new piece of information or event on all devices. In this paper we first present a study investigating the factors influencing users' decisions in handling notifications in a multi-device environment. We collected 57,242 in-the-wild notifications from 24 users over a period of 21 days. We found that users' decisions in handling notifications are impacted by their physical activity, location, network connectivity, application category and the device used for handling the previous notification. Finally, we show that an individualized model can predict the device on which the user will handle a notification in the given context with 82% specificity and 91% sensitivity.
{"title":"NotifyMeHere: Intelligent Notification Delivery in Multi-Device Environments","authors":"Abhinav Mehrotra, R. Hendley, Mirco Musolesi","doi":"10.1145/3295750.3298932","DOIUrl":"https://doi.org/10.1145/3295750.3298932","url":null,"abstract":"Personal interactions and information access are happening more and more through the mediation of computing devices of various types all around us. In our daily life we use many computing devices running different versions of the same application such as email clients or social media platforms, which alert users about a new piece of information or event on all devices. In this paper we first present a study investigating the factors influencing users' decisions in handling notifications in a multi-device environment. We collected 57,242 in-the-wild notifications from 24 users over a period of 21 days. We found that users' decisions in handling notifications are impacted by their physical activity, location, network connectivity, application category and the device used for handling the previous notification. Finally, we show that an individualized model can predict the device on which the user will handle a notification in the given context with 82% specificity and 91% sensitivity.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"58 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":"114299642","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}
Scientific search engines enrich result lists with indicators of scientific quality, such as citation counts, in order to enhance user experience and improve performance. This dissertation project aims to study the effect of such integration of impact indicators in search result pages on relevance evaluation, considering both traditional citation-based and altmetric measures. The user behavior is analyzed with a combination of eye-tracking and think-aloud as well as a questionnaire. This paper describes the methodology of an experimental study designed to answer the question if the visibility of metrics alters the behavior during scientific literature search and if behavior varies depending on the kind of metric, specifically citation rate and altmetric score.
{"title":"Do Metrics Matter?","authors":"Jacqueline Sachse","doi":"10.1145/3295750.3298973","DOIUrl":"https://doi.org/10.1145/3295750.3298973","url":null,"abstract":"Scientific search engines enrich result lists with indicators of scientific quality, such as citation counts, in order to enhance user experience and improve performance. This dissertation project aims to study the effect of such integration of impact indicators in search result pages on relevance evaluation, considering both traditional citation-based and altmetric measures. The user behavior is analyzed with a combination of eye-tracking and think-aloud as well as a questionnaire. This paper describes the methodology of an experimental study designed to answer the question if the visibility of metrics alters the behavior during scientific literature search and if behavior varies depending on the kind of metric, specifically citation rate and altmetric score.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"13 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":"114112704","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}
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.
{"title":"Investigating Result Presentation in Conversational IR","authors":"Souvick Ghosh","doi":"10.1145/3295750.3298974","DOIUrl":"https://doi.org/10.1145/3295750.3298974","url":null,"abstract":"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.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"458 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":"121181119","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}
Johannes Kiesel, Arefeh Bahrami, Benno Stein, Avishek Anand, Matthias Hagen
Queries containing false memories (i.e., attributes the user misremembered about a searched item) represent a challenge for search systems. A query with a false memory will match inadequate results or even no result, and an automatic query correction is necessary to satisfy the user expectations. For voice-based search interfaces, which aim at a natural, dialog-based search experience, a sensible answer to this kind of unintentionally ill-posed queries is even more crucial. However, the usual solutions in display-based interfaces for queries without matches (e.g., suggesting to drop some query terms) cannot really be transferred to the voice-based setting. Based on the assumption that false memory queries could be identified---a research problem in its own right---, we present the first user study on how voice-based search systems may communicate the respective corrections to a user. Our study compares the user satisfaction in a voice-based search setting for three kinds of false memory clarifications and a baseline case where the system just answers "I don't know.'' Our findings suggest that (1)~users are more satisfied when they receive a clarification that and how the system corrected a false memory, (2)~users even prefer failed correction attempts over no such attempt, and (3)~the tone of the clarification has to be considered for the best possible user satisfaction as well.
{"title":"Clarifying False Memories in Voice-based Search","authors":"Johannes Kiesel, Arefeh Bahrami, Benno Stein, Avishek Anand, Matthias Hagen","doi":"10.1145/3295750.3298961","DOIUrl":"https://doi.org/10.1145/3295750.3298961","url":null,"abstract":"Queries containing false memories (i.e., attributes the user misremembered about a searched item) represent a challenge for search systems. A query with a false memory will match inadequate results or even no result, and an automatic query correction is necessary to satisfy the user expectations. For voice-based search interfaces, which aim at a natural, dialog-based search experience, a sensible answer to this kind of unintentionally ill-posed queries is even more crucial. However, the usual solutions in display-based interfaces for queries without matches (e.g., suggesting to drop some query terms) cannot really be transferred to the voice-based setting. Based on the assumption that false memory queries could be identified---a research problem in its own right---, we present the first user study on how voice-based search systems may communicate the respective corrections to a user. Our study compares the user satisfaction in a voice-based search setting for three kinds of false memory clarifications and a baseline case where the system just answers \"I don't know.'' Our findings suggest that (1)~users are more satisfied when they receive a clarification that and how the system corrected a false memory, (2)~users even prefer failed correction attempts over no such attempt, and (3)~the tone of the clarification has to be considered for the best possible user satisfaction as well.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"19 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":"131351920","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}
Sandeep Avula, Jaime Arguello, Robert G. Capra, Jordan Dodson, Yuhui Huang, Filip Radlinski
Popular messaging platforms such as Slack have given rise to thousands of applications (or bots) that users can engage with individually or as a group. In this paper, we study the use of searchbots (i.e., bots that perform specific types of searches) during collaborative information-seeking tasks mediated through Slack. We report on a user study in which 27 pairs of participants were exposed to three searchbot conditions (a within-subjects design). In the first condition, participants completed the task by searching independently and coordinating through Slack (no searchbot). In the second condition, participants could only search inside of Slack using the searchbot. In the third condition, participants could both search inside of Slack using the searchbot and outside of Slack using their own independent search interfaces. We investigate four research questions focusing on the influence of the searchbot condition on outcomes associated with: (RQ1) participants' levels of workload, (RQ2) collaborative awareness, (RQ3) experiences interacting with the searchbot, and (RQ4) search behaviors. Our results suggest opportunities and challenges in designing searchbots to support collaborative search. On one hand, access to the searchbot resulted in more collaborative awareness, ease of coordination, and fewer duplicated searches. On the other hand, forcing participants to share the querying environment resulted in fewer overall queries, fewer query refinements by individuals, and greater levels of effort. We discuss the implications of our findings for designing effective searchbots to support collaborative search.
{"title":"Embedding Search into a Conversational Platform to Support Collaborative Search","authors":"Sandeep Avula, Jaime Arguello, Robert G. Capra, Jordan Dodson, Yuhui Huang, Filip Radlinski","doi":"10.1145/3295750.3298928","DOIUrl":"https://doi.org/10.1145/3295750.3298928","url":null,"abstract":"Popular messaging platforms such as Slack have given rise to thousands of applications (or bots) that users can engage with individually or as a group. In this paper, we study the use of searchbots (i.e., bots that perform specific types of searches) during collaborative information-seeking tasks mediated through Slack. We report on a user study in which 27 pairs of participants were exposed to three searchbot conditions (a within-subjects design). In the first condition, participants completed the task by searching independently and coordinating through Slack (no searchbot). In the second condition, participants could only search inside of Slack using the searchbot. In the third condition, participants could both search inside of Slack using the searchbot and outside of Slack using their own independent search interfaces. We investigate four research questions focusing on the influence of the searchbot condition on outcomes associated with: (RQ1) participants' levels of workload, (RQ2) collaborative awareness, (RQ3) experiences interacting with the searchbot, and (RQ4) search behaviors. Our results suggest opportunities and challenges in designing searchbots to support collaborative search. On one hand, access to the searchbot resulted in more collaborative awareness, ease of coordination, and fewer duplicated searches. On the other hand, forcing participants to share the querying environment resulted in fewer overall queries, fewer query refinements by individuals, and greater levels of effort. We discuss the implications of our findings for designing effective searchbots to support collaborative search.","PeriodicalId":187771,"journal":{"name":"Proceedings of the 2019 Conference on Human Information Interaction and Retrieval","volume":"24 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":"132937631","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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