Investigating the role of in-situ user expectations in Web search

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.     

Combining different evaluation systems on social media for measuring user satisfaction

Web 2.0 allows people to express and share their opinions about products and services they buy/use. These opinions can be expressed in various ways: numbers, texts, emoticons, pictures, videos, audios, and so on. There has been great interest in the strategies for extracting, organising and analysing this kind of information. In a social media mining framework, in particular, the use of textual data has been explored in depth and still represents a challenge. On a rating and review website, user satisfaction can be detected both from a rating scale and from the written text. However, in common practice, there is a lack of algorithms able to combine judgments provided with both comments and scores. In this paper we propose a strategy to jointly measure the user evaluations obtained from the two systems. Text polarity is detected with a sentiment-based approach, and then combined with the associated rating score. The new rating scale has a finer granularity. Moreover, also enables the reviews to be ranked. We show the effectiveness of our proposal by analysing a set of reviews about the Uffizi Gallery in Florence (Italy) published on TripAdvisor.     

The influence of personalization on tag query length in social media search

Social content systems contain enormous collections of unstructured user-generated content, annotated by the collaborative effort of regular Internet users. Tag-clouds have become popular interfaces that allow users to query the database of these systems by clicking relevant terms. However, these single click queries are often not expressive enough to effectively retrieve the desired content. Users have to use multiple clicks or type longer queries to satisfy their information need.     

Supervised sentiment analysis in Czech social media

This article describes in-depth research on machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in the case of the Czech language no systematic research has yet been conducted. We tackle this issue and establish a common ground for further research by providing a large human-annotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. We also experiment with five different feature selection algorithms and investigate the influence of named entity recognition and preprocessing on sentiment classification performance. Moreover, in addition to our newly created social media dataset, we also report results for other popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition, potentially leading to the production of high-end commercial solutions.     

Characterizing multi-domain false news and underlying user effects on Chinese Weibo

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specific domains (like politics or health care), little work is found comparing false news across domains. In this article, we investigate false news across nine domains on Weibo, the largest Twitter-like social media platform in China, from 2009 to 2019. The newly collected data comprise 44,728 posts in the nine domains, published by 40,215 users, and reposted over 3.4 million times. Based on the distributions and spreads of the multi-domain dataset, we observe that false news in domains that are close to daily life like health and medicine generated more posts but diffused less effectively than those in other domains like politics, and that political false news had the most effective capacity for diffusion. The widely diffused false news posts on Weibo were associated strongly with certain types of users — by gender, age, etc. Further, these posts provoked strong emotions in the reposts and diffused further with the active engagement of false-news starters. Our findings have the potential to help design false news detection systems in suspicious news discovery, veracity prediction, and display and explanation. The comparison of the findings on Weibo with those of existing work demonstrates nuanced patterns, suggesting the need for more research on data from diverse platforms, countries, or languages to tackle the global issue of false news. The code and new anonymized dataset are available at https://github.com/ICTMCG/Characterizing-Weibo-Multi-Domain-False-News.     

Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis

Social media data have recently attracted considerable attention as an emerging voice of the customer as it has rapidly become a channel for exchanging and storing customer-generated, large-scale, and unregulated voices about products. Although product planning studies using social media data have used systematic methods for product planning, their methods have limitations, such as the difficulty of identifying latent product features due to the use of only term-level analysis and insufficient consideration of opportunity potential analysis of the identified features. Therefore, an opportunity mining approach is proposed in this study to identify product opportunities based on topic modeling and sentiment analysis of social media data. For a multifunctional product, this approach can identify latent product topics discussed by product customers in social media using topic modeling, thereby quantifying the importance of each product topic. Next, the satisfaction level of each product topic is evaluated using sentiment analysis. Finally, the opportunity value and improvement direction of each product topic from a customer-centered view are identified by an opportunity algorithm based on product topics’ importance and satisfaction. We expect that our approach for product planning will contribute to the systematic identification of product opportunities from large-scale customer-generated social media data and will be used as a real-time monitoring tool for changing customer needs analysis in rapidly evolving product environments.     

Exploring features for the automatic identification of user goals in web search

Queries submitted to search engines can be classified according to the user goals into three distinct categories: navigational, informational, and transactional. Such classification may be useful, for instance, as additional information for advertisement selection algorithms and for search engine ranking functions, among other possible applications. This paper presents a study about the impact of using several features extracted from the document collection and query logs on the task of automatically identifying the users’ goals behind their queries. We propose the use of new features not previously reported in literature and study their impact on the quality of the query classification task. Further, we study the impact of each feature on different web collections, showing that the choice of the best set of features may change according to the target collection.     

Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach

Social Networking Sites (SNSs) play an important role in our daily lives and the number of their users increases regularly. To understand how users can be satisfied in the complex digital environment of SNSs, this study examines how motivations and emotions combine with each other to explain high satisfaction. Users’ motivations comprise four attributes, entertainment, information, social-psychological, and convenience. Emotions are divided into their two main categories, that is positive and negative emotions. We draw on complexity and configuration theories, present a conceptual model along with propositions and perform a fuzzy-set qualitative comparative analysis (fsQCA). Through an empirically study with 582 SNSs users, we present eight combinations (configurations) of motivations and emotions that lead to high satisfaction, which highlight the role of high convenience, followed by entertainment and information motivations in being satisfied with SNSs. High satisfaction can be achieved both when positive and negative emotions are high and low, depending on how they combine users’ motivations. None of the factors are indispensable to explain high satisfaction on their own, instead they are insufficient but necessary parts of the causal combinations that explain high satisfaction. This study contributes in SNSs literature by extending current knowledge on how motivations and emotions combine to increase satisfaction, and by identifying specific patterns of users for whom these factors are important and influence greatly their satisfaction.     

Cultivating the sense of belonging and motivating user participation in virtual communities: A social capital perspective

Virtual communities (VCs) are attracting more attention as they provide a platform for people to share experiences and knowledge, which may further impact their purchase decisions. From a social capital perspective, this study investigated factors that cultivate a VC member's sense of belonging and their effects on facilitating his participation in the VC in terms of the intentions to get and share experiences and knowledge. The results indicated that three factors that relate to three dimensions of social capital in the VC – familiarity with members in the VC from the structural dimension, perceived similarity with other members from the cognitive dimension, and trust in other members from the relational dimension – are all positively related to the sense of belonging, which affects intentions to get and share knowledge and mediates the relationships between social capital factors and a VC member's intentions to participate.     

Role of intrinsic and extrinsic factors in user social media acceptance within workspace: Assessing unobserved heterogeneity

This study develops and empirically tests a theoretical extension of a technology acceptance model that integrates intrinsic and extrinsic motivators into IT acceptance to predict the adoption of social media within the workspace. The model was tested using cross-sectional data collected from different workplaces in different geographic regions. To detect the homogeneity of users’ behavior, we used a response-based procedure for partial least squares. The model was strongly supported for the global model. Our results revealed the existence of distinct adoption behaviors for different groups within the overall sample. These findings advance theory and contribute to future research on social media adoption.     

Computing controversy: Formal model and algorithms for detecting controversy on Wikipedia and in search queries

Controversy is a complex concept that has been attracting attention of scholars from diverse fields. In the era of Internet and social media, detecting controversy and controversial concepts by the means of automatic methods is especially important. Web searchers could be alerted when the contents they consume are controversial or when they attempt to acquire information on disputed topics. Presenting users with the indications and explanations of the controversy should offer them chance to see the “wider picture” rather than letting them obtain one-sided views. In this work we first introduce a formal model of controversy as the basis of computational approaches to detecting controversial concepts. Then we propose a classification based method for automatic detection of controversial articles and categories in Wikipedia. Next, we demonstrate how to use the obtained results for the estimation of the controversy level of search queries. The proposed method can be incorporated into search engines as a component responsible for detection of queries related to controversial topics. The method is independent of the search engine’s retrieval and search results recommendation algorithms, and is therefore unaffected by a possible filter bubble.Our approach can be also applied in Wikipedia or other knowledge bases for supporting the detection of controversy and content maintenance. Finally, we believe that our results could be useful for social science researchers for understanding the complex nature of controversy and in fostering their studies.     

学术期刊全媒体科学普及传播实践

【目的】 以《大气科学进展》全媒体科学普及传播实践经验为例,探讨学术期刊借助全媒体手段普及科学知识的方法、意义和努力方向。【方法】 分析《大气科学进展》近几年开展科学普及传播的手段和方法,总结学术期刊加强科普能力建设的实践经验。【结果】 科学普及对于推动科技发展和社会文明进步具有重要意义。学术期刊应当重视科学普及,利用社交媒体平台、期刊封面图片和多语种科学新闻等方式,为全世界读者服务,传播科学知识,提高公众科学素养,推动学术交流,促进科技创新。【结论】 要建设具有国际影响力的高水平学术期刊,必须重视科学知识的普及,通过全媒体平台,向社会大众传播最新研究成果,扩大学术成果的传播范围,提高学术成果在国际上的显示度和影响力。