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《Information processing & management》2023,60(3):103300
Pre-adoption expectations often serve as an implicit reference point in users’ evaluation of information systems and are closely associated with their goals of interactions, behaviors, and overall satisfaction. Despite the empirically confirmed impacts, users’ search expectations and their connections to tasks, users, search experiences, and behaviors have been scarcely studied in the context of online information search. To address the gap, we collected 116 sessions from 60 participants in a controlled-lab Web search study and gathered direct feedback on their in-situ expected information gains (e.g., number of useful pages) and expected search efforts (e.g., clicks and dwell time) under each query during search sessions. Our study aims to examine (1) how users’ pre-search experience, task characteristics, and in-session experience affect their current expectations and (2) how user expectations are correlated with search behaviors and satisfaction. Our results with both quantitative and qualitative evidence demonstrate that: (1) user expectation is significantly affected by task characteristics, previous and in-situ search experience; (2) user expectation is closely associated with users’ browsing behaviors and search satisfaction. The knowledge learned about user expectation advances our understanding of users’ search behavioral patterns and their evaluations of interaction experience and will also facilitate the design, implementation, and evaluation of expectation-aware user models, metrics, and information retrieval (IR) systems. 相似文献
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Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for accurate learning. While unlabeled documents are easily collected and plentiful, labeled documents are difficultly generated because a labeling task must be done by human developers. In this paper, we propose a new text classification method based on unsupervised or semi-supervised learning. The proposed method launches text classification tasks with only unlabeled documents and the title word of each category for learning, and then it automatically learns text classifier by using bootstrapping and feature projection techniques. The results of experiments showed that the proposed method achieved reasonably useful performance compared to a supervised method. If the proposed method is used in a text classification task, building text classification systems will become significantly faster and less expensive. 相似文献
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Mouna Torjmen-Khemakhem Karen Pinel-Sauvagnat Mohand Boughanem 《Information processing & management》2013
Multimedia objects can be retrieved using their context that can be for instance the text surrounding them in documents. This text may be either near or far from the searched objects. Our goal in this paper is to study the impact, in term of effectiveness, of text position relatively to searched objects. The multimedia objects we consider are described in structured documents such as XML ones. The document structure is therefore exploited to provide this text position in documents. Although structural information has been shown to be an effective source of evidence in textual information retrieval, only a few works investigated its interest in multimedia retrieval. More precisely, the task we are interested in this paper is to retrieve multimedia fragments (i.e. XML elements having at least one multimedia object). Our general approach is built on two steps: we first retrieve XML elements containing multimedia objects, and we then explore the surrounding information to retrieve relevant multimedia fragments. In both cases, we study the impact of the surrounding information using the documents structure. 相似文献
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Sven H. Koch Rumyana Proynova Barbara Paech Thomas Wetter 《Ethics and Information Technology》2013,15(1):45-61
Software users have different sets of personal values, such as benevolence, self-direction, and tradition. Among other factors, these personal values influence users’ emotions, preferences, motivations, and ways of performing tasks—and hence, information needs. Studies of user acceptance indicate that personal traits like values and related soft issues are important for the user’s approval of software. If a user’s dominant personal value were known, software could automatically show an interface variant which offers information and functionality that best matches his or her dominant value. A user’s dominant personal value is the one that most strongly influences his or her attitudes and behaviors. However, existing methods for measuring a user’s values are work intensive and/or interfere with the user’s privacy needs. If interface tailoring for very large groups of users is planned, value approximation has to be achieved on a large scale to assign individualized software to all users of the software. Our work focuses on approximating the dominant values of a user with less effort and less impact on privacy. Instead of probing for a user’s values directly, we explore the potential of approximating these values based on the user’s preferences for key tasks. Producing tailored versions of software is a separate topic not in the focus here. In this paper we rather describe a method to identify user values from task preferences and an empirical study of applying parts of this method. We are proposing the method in this paper for the first time except for a preliminary version orally presented at a workshop. The method consists of a research process and an application process. In the research process a researcher has to identify key tasks occurring in a context under investigation which have a relationship to personal values. These key tasks can be used in the application process to approximate the dominant values of new users in a similar context. In this empirical study we show that the research process of our method allows us to determine key tasks which approximate values in the shared context of nursing. The majority of the nurses were found to have one of the three following dominant values: benevolence, self-direction, or hedonism. Data confirmed common expectations: that nurses with the value of benevolence, when compared to all other nurses, had a higher preference for tasks which helped people immediately or improved their circumstances of the treatment. In relation to all other nurses, participants with self-direction disliked tasks which affected their personal freedom, and users with hedonism had a lower preference for tasks which involved physical work and preferred tasks which promised gratification. Our findings advance measurement of personal values in large user groups by asking questions with less privacy concern. However, the method requires substantial efforts during the initial research process to prepare such measurements. Future work includes replicating our method in other contexts and identifying value-dependent tasks for users with other values than the three values our empirical study mainly focused on. 相似文献
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Shanshan Lyu Wentao Ouyang Huawei Shen Xueqi Cheng 《Information processing & management》2019,56(4):1484-1493
The problem of quality estimation of crowdsourced work is of great importance. Although a variety of aggregation methods have been proposed to find high-quality structured claims in multiple-choice crowdsourcing tasks such as item labeling, they do not apply to more general tasks, such as article writing and brand design with unstructured submissions. One possibility to tackle this problem is to ask another set of crowd workers to review and grade each submission, essentially transforming unstructured submissions into structured ratings. Nevertheless, such an approach incurs unnecessary monetary cost and delay. In this paper, we address this problem by exploiting task requesters’ historical feedback and directly modeling the submission quality. We propose two embedding-based methods where the first one learns worker embedding and the second one learns both worker embedding and meta information embedding, with additional consideration of neighborhood similarity. Experimental results on three large-scale crowdsourcing data sets demonstrate that our embedding-based feature-learning methods perform much better than feature-engineering methods that use popular learning-to-rank algorithms. At the same time, our methods do not require additional crowdsourced grading. 相似文献
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Sophie Rutter Verena Blinzler Chaoyu Ye Max L. Wilson Michael D. Twidale 《Information processing & management》2019,56(3):919-938
With greater access to computational resources, people use search to address many everyday challenges in their lives, including solving technology problems. Although there are now many useful ‘how-to’ resources online (especially videos on YouTube), it can still be difficult to identify, understand, and resolve certain kinds of technical problem. While research tasks have been studied for many years and we know the tactics people use, we know far less about searchers’ tactics for how-to technical tasks that involve actually being able to apply found information to resolve a problem. Crucial to our study was developing and studying a highly realistic, how-to technical task, for which there was no single guidance resource: making a phone safe for a child. After providing 39 participants with an actual phone to fix, and a search engine to perform the task, we analysed their search tactics using retrospective cued think aloud interviews. Our primary contribution is a set of 77 tactics used, in three categories, along with detail of how common they were. We conclude that people had a lot of tactics in their repertoire. Although it was not hard for participants to find relevant information, what was hard was for participants to find information they could use; indeed only 23% of participants successfully completed the entire task. Domain knowledge affected the choice of tactics used (although not necessarily towards better task success). We discuss these influences and make design recommendations for how future search systems can support those in resolving how-to technical tasks. 相似文献
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《Information processing & management》2019,56(3):704-720
This study aims to explore the relationships between user interaction and digital libraries (DLs) evaluation. User interaction is a multi-dimensional construct and recognized as three dimensions in this study, as user interaction with: information resource; interface; and, tasks. DL evaluation is considered from the user's perspective and defined as users’ perception of DL performance from different perspectives, including the support of DL's interaction design to user interaction (labeled as interaction-design-based (IDB) evaluation), the support of task completion (labeled as task-based evaluation), and a DL's overall performance (labeled as overall evaluation). An experiment with 48 participants was conducted using the China National Knowledge Infrastructure (CNKI (http://cnki.net/), the most widely used digital library in China). Participants searched for four simulated work tasks and one real work task during the experiment, subsequently evaluating their interaction with information resource, interface, and tasks, and DL performance from different perspectives before or after the search. Correlation analysis and stepwise regression analysis were conducted to examine the relationships. The results indicate that a list of factors related to different dimensions of user interaction can significantly predict or be correlated to users’ evaluation of DL performance from different perspectives, including appropriateness, rich and valid links, reasonable page layout, salience of topics, search task difficulty, well-organized web site, easy to learn, accessibility, usefulness, familiarity with task procedure, etc. These factors surface as the most critical criteria for DL evaluation. Based on the results, an integrated DL evaluation framework is developed. The study adds new knowledge about how tasks affect DL evaluation. It has implications for improving the efficiency of DL evaluation and helping DL developers design DLs to better support users’ interaction, task completion, and their overall experience with DLs. 相似文献
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《Information processing & management》2022,59(1):102792
Medical crowdfunding helps low-income patients raise money for medical treatment and has grown tremendously in recent years. The most appropriate messaging strategy for writing charitable appeals to attract donations remains unclear. This study fills this gap by drawing on Aristotle's three modes of persuasion to explore factors affecting willingness to donate to medical crowdfunding projects from three aspects: logos, pathos, and ethos. This study adopted a multi-method approach by conducting two laboratory experiments (N = 125 and N = 123) and a field study (N = 1645). Analysis of variance (ANOVA) in Study 1 showed that high information quality (F = 9.774, p = 0.002) and gain frame (F = 8.620, p = 0.004) have positive effects on the trustworthiness of the project initiator (ethos), which in turn promoting potential donors’ willingness to donate (β = 0.339, p = 0.001). Study 2 confirmed the findings about information quality in Study 1, and further show that there was no significant difference between gain-first and gain-last frame on trustworthiness and willingness to donate (p > 0.05). Then, information quality is further detailed into three sub-dimensions in Study 3: text length, number of images, and number of health-related words. The results of ordinary least squares (OLS) regression with robust standard error indicate that the text length (β = 0.350, p < 0.001) and number of images (β = 0.048, p < 0.001) positively influence donation behavior, but the opposite conclusion yields health-related words (β = -0.027, p < 0.01). This study provides theoretical insights into the role of medical crowdfunding charitable appeals by verifying the persuasion effects of rational, emotional, and credibility appeals. This study also contributes to persuasion theory by highlighting the role of emotional appeals and identifying the mediating impact of credibility appeals in the context of medical crowdfunding. This study also has important practical implications by guiding funders to write persuasive charity appeals that will attract the attention of potential donors. 相似文献
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Effective knowledge management in a knowledge-intensive environment can place heavy demands on the information filtering (IF) strategies used to model workers’ long-term task-needs. Because of the growing complexity of knowledge-intensive work tasks, a profiling technique is needed to deliver task-relevant documents to workers. In this study, we propose an IF technique with task-stage identification that provides effective codification-based support throughout the execution of a task. Task-needs pattern similarity analysis based on a correlation value is used to identify a worker’s task-stage (the pre-focus, focus formulation, or post-focus task-stage). The identified task-stage is then incorporated into a profile adaptation process to generate the worker’s current task profile. The results of a pilot study conducted in a research institute confirm that there is a low or negative correlation between search sessions and transactions in the pre-focus task-stage, whereas there is at least a moderate correlation between search sessions/transactions in the post-focus stage. Compared with the traditional IF technique, the proposed IF technique with task-stage identification achieves, on average, a 19.49% improvement in task-relevant document support. The results confirm the effectiveness of the proposed method for knowledge-intensive work tasks. 相似文献
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Michael J. Cole Jacek Gwizdka Chang Liu Nicholas J. Belkin Xiangmin Zhang 《Information processing & management》2013
The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n = 40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n = 409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session. 相似文献
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The nature of the task that leads a person to engage in information interaction, as well as of information seeking and searching tasks, have been shown to influence individuals’ information behavior. Classifying tasks in a domain has been viewed as a departure point of studies on the relationship between tasks and human information behavior. However, previous task classification schemes either classify tasks with respect to the requirements of specific studies or merely classify a certain category of task. Such approaches do not lead to a holistic picture of task since a task involves different aspects. Therefore, the present study aims to develop a faceted classification of task, which can incorporate work tasks and information search tasks into the same classification scheme and characterize tasks in such a way as to help people make predictions of information behavior. For this purpose, previous task classification schemes and their underlying facets are reviewed and discussed. Analysis identifies essential facets and categorizes them into Generic facets of task and Common attributes of task. Generic facets of task include Source of task, Task doer, Time, Action, Product, and Goal. Common attributes of task includes Task characteristics and User’s perception of task. Corresponding sub-facets and values are identified as well. In this fashion, a faceted classification of task is established which could be used to describe users’ work tasks and information search tasks. This faceted classification provides a framework to further explore the relationships among work tasks, search tasks, and interactive information retrieval and advance adaptive IR systems design. 相似文献
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[目的/意义]探究差异性任务情境对用户移动诊疗信息搜索行为的影响，并基于对比分析为移动诊疗信息的产品和服务创新提供改进方法和策略。[方法/过程]选取事实型、解释型和探索型3种代表性任务情境，采用"情境实验+问卷访谈"的方法获取研究样本的行为特征数据并辅助以录屏软件全程记录实验过程，通过人工编码、数据清洗和数据预处理等步骤对收集到的问卷文本和视频数据进行处理，获得36个有效样本和108份有效数据并利用SPSS22.0进行Mann-Whitney非参数检验。[结果/结论]差异性任务情境对搜索过程变量均具有显著影响。相对于事实型任务，解释型任务和探索型任务不仅在查询串修改次数、访问网页数和点击链接数上显著更多，而且在搜索策略上也倾向于使用更多的搜索渠道。任务类型对用户在任务完成前后的感知任务难度、感知完成度和客观完成度等指标上有显著影响，其中事实型任务难度最低且客观完成度最高，任务类型对用户的任务认知和感知多渠道搜索有用性无显著影响。 相似文献
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《Information processing & management》2023,60(2):103234
Detecting suicidal tendencies and preventing suicides is an important social goal. The rise and continuance of emotion, the emotion category, and the intensity of the emotion are important clues about suicidal tendencies. The three determinants of emotion, viz. Valence, Arousal, and Dominance (VAD) can help determine a person’s exact emotion(s) and its intensity. This paper introduces an end-to-end VAD-assisted transformer-based multi-task network for detecting emotion (primary task) and its intensity (auxiliary task) in suicide notes. As part of this research, we expand the utility of the emotion-annotated benchmark dataset of suicide notes, CEASE-v2.0, by annotating all its sentences with emotion intensity labels. Empirical results show that our multi-task method performs better than the corresponding single-task systems, with the best attained overall Mean Recall (MR) of 65.25% on the emotion task. On a similar task, we improved MR by 8.78% over the existing state-of-the-art system. We evaluated our approach on three benchmark datasets for three different tasks. We observed that the introduced method consistently outperformed existing state-of-the-art approaches on the studied datasets, demonstrating its capacity to generalize to other downstream correlated tasks. We qualitatively examined our model’s output by comparing it to the labeling of a psychiatrist. 相似文献
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《Information processing & management》2020,57(6):102273
Vital to the task of Sentiment Analysis (SA), or automatically mining sentiment expression from text, is a sentiment lexicon. This fundamental lexical resource comprises the smallest sentiment-carrying units of text, words, annotated for their sentiment properties, and aids in SA tasks on larger pieces of text. Unfortunately, digital dictionaries do not readily include information on the sentiment properties of their entries, and manually compiling sentiment lexicons is tedious in terms of annotator time and effort. This has resulted in the emergence of a large number of research works concentrated on automated sentiment lexicon generation. The dictionary-based approach involves leveraging digital dictionaries, while the corpus-based approach involves exploiting co-occurrence statistics embedded in text corpora. Although the former approach has been exhaustively investigated, the majority of works focus on terms. The few state-of-the-art models concentrated on the finer-grained term sense level remain to exhibit several prominent limitations, e.g., the proposed semantic relations algorithm retrieves only senses that are at a close proximity to the seed senses in the semantic network, thus prohibiting the retrieval of remote sentiment-carrying senses beyond the reach of the ‘radius’ defined by number of iterations of semantic relations expansion. The proposed model aims to overcome the issues inherent in dictionary-based sense-level sentiment lexicon generation models using: (1) null seed sets, and a morphological approach inspired by the Marking Theory in Linguistics to populate them automatically; (2) a dual-step context-aware gloss expansion algorithm that ‘mines’ human defined gloss information from a digital dictionary, ensuring senses overlooked by the semantic relations expansion algorithm are identified; and (3) a fully-unsupervised sentiment categorization algorithm on the basis of the Network Theory. The results demonstrate that context-aware in-gloss matching successfully retrieves senses beyond the reach of the semantic relations expansion algorithm used by prominent, well-known models. Evaluation of the proposed model to accurately assign senses with polarity demonstrates that it is on par with state-of-the-art models against the same gold standard benchmarks. The model has theoretical implications in future work to effectively exploit the readily-available human-defined gloss information in a digital dictionary, in the task of assigning polarity to term senses. Extrinsic evaluation in a real-world sentiment classification task on multiple publically-available varying-domain datasets demonstrates its practical implication and application in sentiment analysis, as well as in other related fields such as information science, opinion retrieval and computational linguistics. 相似文献
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【目的/意义】在线健康社区用户规模庞大,信息量浩如烟海,如何帮助社区管理者和用户判别有用信息,提
高决策效率是亟待解决的问题。【方法/过程】在复杂网络视角下,提出一个新的评论有用性分析框架。首先,采集
在线健康社区患者评论数据,采用文本分析法分析有用评论、非有用评论以及所有评论的主题分布和情感分布,初
步分析各类评论文本的有用性特征;其次,将各类评论文本分别转换为文本关联网络,使用社会网络分析方法进一
步分析其有用性特征;最后,分析评论有用性及其特征与患者发表评论、用户对评论的有用性投票以及文本关联网
络结构特征的关联性,实现基于文本关联网络的评论有用性分析。【结果/结论】有用评论和非有用评论文本关联网
络结构具有一定差异,在线健康社区用户就诊前后的信息需求和经验输出的重点有所不同。【创新/局限】基于复杂
网络视角研究在线健康社区评论有用性,但仅使用了好大夫在线的数据,未来可对更多数量和种类的在线健康社
区信息内容有用性进行研究。 相似文献
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Document retrieval systems based on probabilistic or fuzzy logic considerations may order documents for retrieval. Users then examine the ordered documents until deciding to stop, based on the estimate that the highest ranked unretrieved document will be most economically not retrieved. We propose an expected precision measure useful in estimating the performance expected if yet unretrieved documents were to be retrieved, providing information that may result in more economical stopping decisions. An expected precision graph, comparing expected precision versus document rank, may graphically display the relative expected precision of retrieved and unretrieved documents and may be used as a stopping aid for online searching of text data bases. The effectiveness of relevance feedback may be examined as a search progresses. Expected precision values may also be used as a cutoff for systems consistent with probabilistic models operating in batch modes. Techniques are given for computing the best expected precision obtainable and the expected precision of subject neutral documents. 相似文献