Support Information Search Research Articles

“Sorry, too much information”—Designing online review systems that support information search and processing

When faced with a large number of reviews, customers can easily be overwhelmed by information overload. To address this problem, review systems have introduced design features aimed at improving the scanning, reading, and processing of online reviews. Though previous research has examined the effect of selected design features on information overload, a comprehensive and up-to-date overview of these features remains outstanding. We therefore develop and evaluate a taxonomy for information search and processing in online review systems. Based on a sample of 65 review systems, drawn from a variety of online platform environments, our taxonomy presents 50 distinct characteristics alongside the knowledge status quo of the features currently implemented. Our study enables both scholars and practitioners to better understand, compare and further analyze the (potential) effects that specific design features, and their combinations, have on information overload, and to use these features accordingly to improve online review systems for consumers.

Integrated e-healthcare management system using machine learning and flask

Healthcare is one of the flourishing sectors in each developed and emerging economy. Due to this vast COVID-19 pandemic, the traditional healthcare system cannot provide adequate facilities due to a lack of interactions between doctors and patients. In such conditions, e-healthcare is contributing towards the accelerating growth within the healthcare industry by providing the latest information technology to support information search and communication processes. Besides this, a machine learning algorithm is used to intensify the smartness of the healthcare industry. The five major components of an e-healthcare system are cost-saving, virtual networking, electronic medical record physician-patient relationships and privacy concerns. Our proposed system provides location-based e-prescribing, e-reports, disease prediction, and suggesting treatments and emergency services with a single click, so it is better than another existing system.

Development of Chatbots to Support Web Site Content Search Based on Thematic and Genre Characteristics

The paper considers an approach to creating intelligent assistants in the form of chatbots that support information search based on preliminary genre and thematic clustering of website content. The tasks of finding the necessary in-formation and providing information support to the user, organizing feedback to improve the quality of the search are being solved. A feature of the approach is the use of genre models developed for a given type of resource (educational, informational, etc.), on the basis of which genre structuring of the content of a particular site is carried out. The resulting genre structures allow you to more accurately determine the boundaries of thematic clusters related to the topic of the user's search query. To provide feedback to the user, a simple script has been developed that allows not only to clarify the request, but also to implicitly get information about what exactly did not suit the user in the resulting out-put. An experimental study was conducted on the Telegram platform, the results were compared with the Yandex search engine.

#Covid-19: An exploratory investigation of hashtag usage on Twitter

BackgroundThe literature highlights Twitter as a vital instrument tool for health policy-makers for health communication and promotion. Furthermore, Twitter is a tool allowing us to understand the focus of people regarding a topic of interest. ObjectiveTo provide health policy-makers with insights concerning key topics of interest in the Twitter community regarding Covid-19, and to support information search and health communication. MethodA total of 28.5M tweets have been retrieved, of which 6.9M tweets included hashtags. The data was analyzed using data science and natural language processing libraries. Qualitative analysis was performed using thematic analysis. Results907k different hashtags were used. Of these, only 1192 hashtags were used more than 1000 times. The qualitative analysis resulted in 13 themes. The top three themes regarding the number of hashtags used were related to Covid-19, identifying information, interventions, and geographical tagging. We explored the relationship between themes and showed how health practitioners can understand the communication in relation to specific topics expressed as hashtags (e.g., #stayhome). ConclusionsThe results provide first insights for policy-makers and health practitioners to identify relevant tweets and to choose appropriate hashtags for health communication. The results also show that only with a limited number of Tweets (10 per day) health organizations could have been among the top users.

Adding Semantics to the Legal Domain

Information search and retrieval are part of daily routines of the legal profession. Lawyers, judges, prosecutors, and legal clerks usually access a number of electronic resources to browse, search, select, or update legal contents. Legal databases have currently become large digital libraries where the tasks related to information-seeking may sometimes be cumbersome. Adding semantics to support information search may provide significant results in terms of efficiency, efficacy, and user satisfaction. The main objective of this presentation is to offer several examples of how semantic technologies may be able to improve legal information search in the judicial and lawyers’ domains. However, legal professionals sometimes prefer following routines than changing their information search behavior. New trends in Semantic Web technologies may help to improve both professional and laymen’s skills.

Does pre-activating domain knowledge foster elaborated online information search strategies? Comparisons between young and old web user adults

The present study aimed at investigating how pre-activating prior topic knowledge before browsing the web can support information search performance and strategies of young and older users. The experiment focus on analyzing to what extent prior knowledge pre-activation might cope with older users' difficulties when interacting with a search engine. 26 older (age 60 to 77) and 22 young (age 18 to 32) adults performed 6 information search problems related to health and fantastic movies. Overall, results showed that pre-activating prior topic knowledge increased the time spent evaluating the search engine results pages, fostered deeper processing of the navigational paths elaborated (and thus reduced the exploration of different navigational paths) and improved the semantic specificity of queries. Pre-activating prior knowledge helped older adults produced semantically more specific queries when they had lower prior-knowledge than young adults. Moderation analyses indicated that the pre-activation supported older adults' search performance under the condition that participants generated semantically relevant keywords during this pre-activation task. Implications of these results show that prior topic knowledge pre-activation may be a good lead to support the beneficial role of prior knowledge in older users’ search behavior and performance. Recommendations for design pre-activation support tool are provided.

Click-Search: Supporting information search with crowd-powered image-to-keyword query formulation

Information search is a common yet important task in everyday work and life. It remains challenging how to help users search for information or things they don’t know how to express with words. Also, even when people know how to express, the cognitive cost required to retrieve the concepts and formulate the queries can be excessive. In this paper, we present Click-Search, a search user interface that allows people to indicate their search intents by merely selecting and cropping segments of pictures. The system automatically converts cropped image segments to keywords based on known semantic labels at the level of image pixels that are generated by crowdsourced image tagging. Through a user study, we showed that Click-Search could support object-finding activities effectively with a satisfactory user experience.

Developing Ontology for Arabic Blogs Retrieval

The ability to search for content on the Internet has proven to be essential for many. However, available search engines supporting Arabic language are typically limited to keyword searches and do not take in consideration the underlying semantics of the content. Semantic search engines provide searching and retrieving resources conceptually related to the user informational need. However, the presented technologies support mostly languages using Latin scripts. Arabic is still not well supported. The few works on Arabic Semantic Web applications are derived from the rule governing the traditional Arabic language and not the content available on the Web. In this research, we propose a model for representing Arabic knowledge in the Computer Technology domain using Ontologies. The model starts by elicitation users’ informational needs. Ontologies w ill play a major role in supporting information search and retrieval processes of Arabic blogs on the Web.

Information Search with Mobile Tourist Guides: A Survey of Usage Intention

As mobile tourist guides show huge potentials to support tourists’ information search, executives need to know which mobile systems characterized by which specific functionalities should be provided. The paper proposes an empirical approach combining qualitative and quantitative methods to deduce both, product definition and product marketing information related to mobile tourist guides, respectively. First, a methodology-mix combining qualitative (i.e. explorative survey, focus group interviews) and quantitative techniques (i.e. pilot study, expert interviews) responds to the question ‘Which services should be developed?’ Secondly, again in the pre-prototype phase and based on survey data (N = 705) ideal functionality compositions for promising tourist guides are deduced with regression analysis. Finally, based on technology acceptance theory (Venkatesh et al., 2003a) customer prejudices towards mobile tourist guides are identified. The obtained results support hypotheses indicating strong performance expectations and pronounced effort expectancies towards the deduced concepts designed to support information search by means of mobile tourist guides

CI im Einsatz: Technologien zur Informationssuche, -bewertung und -aufbereitung

Scanning the global business environment for new trends and developments is a critical success factor in Competitive Intelligence (CI). However, due to the ill-defined structure of the business environment - in terms of content, source, and time - seeking for information in the business environment is cumbersome: The limited time and information-processing capacities of managers hamper a full screening of the broad range of relevant aspects. Moreover, details from many different sources have to be combined to receive an interpretable picture of future developments. This paper analyses the structure of the business environment and outlines different information seeking strategies. The paper provides a state-of-the-art review on new technologies for supporting information search in CI and discusses the value added by these tools with respect to the introduced CI challenges.