Mouse interaction data contain a lot of interaction information between users and Search Engine Result Pages (SERPs), which can be useful for evaluating search satisfaction. Existing studies use aggregated features or anchor elements to capture the spatial information in mouse interaction data, which might lose valuable mouse cursor movement patterns for estimating search satisfaction. In this paper

Searching the Internet for a certain topic can become a daunting task because users cannot read and comprehend all the resulting texts. Automatic Text summarization (ATS) in this case is clearly beneficial because manual summarization is expensive and time-consuming. To enhance ATS for single documents, this paper proposes a novel extractive graph-based framework “EdgeSumm” that relies on four proposed

Influence diffusion is extensively studied in social networks for product or service promotion and viral-marketing applications. This paper proposes two models for social influence estimation, namely Time Decay Features Cascade Model (TDF-C) and Time Decay Features Cascade Threshold Model (TDF-CT). These models overcome three main existing challenges - first, measure the strength of user's influence

The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate

A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we

Pseudo-relevance feedback (PRF) is a well-known method for addressing the mismatch between query intention and query representation. Most current PRF methods consider relevance matching only from the perspective of terms used to sort feedback documents, thus possibly leading to a semantic gap between query representation and document representation. In this work, a PRF framework that combines relevance

This study tested how and when leaders’ information seeking from team members influenced team performance and innovation. We developed and examined a model by considering team reflexivity as a mediator and cooperative outcome interdependence as a moderator. The sample comprised 83 work teams and 253 team members from a pharmaceutical firm in China. Multiple-source, time-lagged data were used to test

This paper aims to establish digital forensics and data exploration as a methodology for supporting archival practice and research into a filmmaker's creative processes. We approach this by exploring the digital legacy hard drives of the late artist Stephen Dwoskin (1939–2012), who is recognised as an influential filmmaker at the forefront of the shift from analogue to digital film production. The

Online charitable crowdfunding has become an increasingly popular approach for online fundraising and has attracted widespread concern in society. However, little is known about donation allocation in the implementation of online charitable crowdfunding, especially the purposes involved and the specific flows of these donations. To this end, this study aims to explore project initiators’ fundraising

In recent years, deep learning-based recommender systems have received increasing attention, as deep neural networks can detect important product features in images and text descriptions and capture them in semantic vector representations of items. This is especially relevant for outfit recommendation, since a variety of fashion product features play a role in creating outfits. This work is a comparative

Today, due to a vast amount of textual data, automated extractive text summarization is one of the most common and practical techniques for organizing information. Extractive summarization selects the most appropriate sentences from the text and provide a representative summary. The sentences, as individual textual units, usually are too short for major text processing techniques to provide appropriate

Drawing on the cognitive appraisal theory of emotions and the attribution theory, this study extends existing research by examining how the emotional expressions influence perceived helpfulness of online consumer reviews (OCRs). We include two negative emotions (anger, fear) and two positive emotions (pride, surprise). Each of these four emotions can be described with respect to six emotional appraisal

With the fast growth of e-commerce and the emerging new retail trend—online and offline integration—it is important to recognize the target market and satisfy customers with different needs by analyzing their online search behaviors. Accordingly, we propose sequential search pattern analysis and clustering to analyze consumers’ search behavior throughout the entire shopping process from the perspective

In this paper we introduce HEMOS (Humor-EMOji-Slang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon;

Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical

Deep hashing has been an important research topic for using deep learning to boost performance of hash learning. Most existing deep supervised hashing methods mainly focus on how to effectively preserve the similarity in hash coding solely depending on pairwise supervision. However, such pairwise similarity-preserving strategy cannot fully explore the semantic information in most cases, which results

Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data structures, poor data quality, highly accurate and interpretable services, etc. In this paper, firstly

While users’ discontinuance of use has posed a challenge for social media in recent years, there is a paucity of knowledge on the relationships between different dimensions of overload and how overload adversely affects users’ social media discontinuance behaviors. To address this knowledge gap, this study employed the stressor–strain–outcome (SSO) framework to explain social media discontinuance behaviors

Evidently, online voice of customers (VoC) expressed in social media has emerged as quality data for researchers who are willing to conduct customer-driven business intelligence (BI) research. Nevertheless, to the best of authors’ knowledge, there is still a dearth of studies that deal with such remarkable research stream and address various open data (e.g., social media, intellectual property) from

We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical

In this work, we release a multi-domain and multi-modality event dataset (MMED), containing 25,052 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, BBC News, etc.) and 75,884 image posts shared on Flickr by thousands of social media users. The articles contributed by professional journalists and the images shared by amateur users are annotated according to 410 real-world

Social media is widely used for sharing disaster-related information following natural disasters. Drawing on negativity bias theory, integrated crisis mapping model, and arousal theory, this study characterized the emotional responses of the public and tested the way emotional factors and influential users (with high numbers of followers and activeness) affect the number of reposts. Results indicated

Author co-citation analysis (ACA) is a commonly used method to map knowledge domains and depict scientific intellectual structures. Although all authors’ information has been considered in previous studies, ACA does not distinguish credits of different collaborators within a team. Authors’ sequence in a publication illustrates their contributions and specialty of research, which offers more information

With the increasing growth of video data, especially in cyberspace, video captioning or the representation of video data in the form of natural language has been receiving an increasing amount of interest in several applications like video retrieval, action recognition, and video understanding, to name a few. In recent years, deep neural networks have been successfully applied for the task of video

User location data is valuable for diverse social media analytics. In this paper, we address the non-trivial task of estimating a worldwide city-level Twitter user location considering only historical tweets. We propose a purely unsupervised approach that is based on a synthetic geographic sampling of Google Trends (GT) city-level frequencies of tweet nouns and three clustering algorithms. The approach

Due to the large scale of the typical deployments and the involvement of moving objects to the Internet of Things, participating nodes opportunistically establish data exchanging connections, spanning across multiple organizations and security domains. This opportunistic behavior causes the impossibility of defining valid security policies to rule node authorization, and the ineffectiveness of traditional

Overlapping entity relation extraction has received extensive research attention in recent years. However, existing methods suffer from the limitation of long-distance dependencies between entities, and fail to extract the relations when the overlapping situation is relatively complex. This issue limits the performance of the task. In this paper, we propose an end-to-end neural model for overlapping

This paper presents a novel genetic-based recommender system (BLIGA) that depends on the semantic information and historical rating data. The main contribution of this research lies in evaluating the possible recommendation lists instead of evaluating items then forming the recommendation list. BLIGA utilizes the genetic algorithm to find the best list of items to the active user. Thus, each individual

Increased usage of bots through the Internet in general, and social networks in particular, has many implications related to influencing public opinion. Mechanisms to distinguish humans from machines span a broad spectrum of applications and hence vary in their nature and complexity. Here we use several public Twitter datasets to build a model that can predict whether or not an account is a bot account

Recent advances in semantic web have shown how entity related searches have benefited from entity-based knowledge graphs. However, much of the commonsense knowledge about the real world is in the form of procedures or sequences of actions. Also, search log analysis shows that ‘how-to queries’ make up a significant amount of users’ queries. Unfortunately, these kinds of knowledge are missing from most

Due the advent of new wireless communication, Internet of Things (IoT) and cloud computing, outsourcing the data processing and management to the cloud servers has become a typical transformative computing paradigm. In this context, a cloud auditing protocol is an effective way to check the data integrity for cloud data management. We present a cryptanalysis of two recently proposed certificateless

Information technology-mediated interruptions have become widespread and ubiquitous in the workplace. However, our understanding of how these interruptions and individuals’ interruption processing mechanism impact individuals’ performance, especially psychological performance, is still limited. Leveraging Conservation of Resources Theory, this study focused on two types of IT-mediated information interruptions

The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews

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

The advancements of search engines for traditional text documents have enabled the effective retrieval of massive textual information in a resource-efficient manner. However, such conventional search methodologies often suffer from poor retrieval accuracy especially when documents exhibit unique properties that behoove specialized and deeper semantic extraction. Recently, AlgorithmSeer, a search engine

Information extraction is one of the important tasks in the field of Natural Language Processing (NLP). Most of the existing methods focus on general texts and little attention is paid to information extraction in specialized domains such as legal texts. This paper explores the task of information extraction in the legal field, which aims to extract evidence information from court record documents

A reasonable value assessment method plays an important role in both companies’ internal management and their external transactions. Traditional value assessment methods like the relative valuation method need historical financial indicators and some market multipliers but ignore non-financial information included in the text. Startup companies are usually listed before they start to make a profit

Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing

How to parse the human image to obtain the text label corresponding to the human body is a critical task for human-computer interaction. Although previous methods have significantly improved the parsing performance, the problem of parsing confusion and tiny target missing remains unresolved, which leads to errors and incomplete inference accordingly. Targeting at these drawbacks, we fuse semantic and

Transformative computing in the fourth-generation industrialization, receives all signals and all sequences from sensing devices under artificial intelligence in wireless networking. Then the system has to combine them and make a useful information for human. In an industrial building or in a home, many electronic devices would be using and they make various energy signals and sequences. The devices

The possibilities that emerge from micro-blogging generated content for crisis-related situations make automatic crisis management using natural language processing techniques a hot research topic. Our aim here is to contribute to this line of research focusing for the first time on French tweets related to ecological crises in order to support the French Civil Security and Crisis Management Department

In this paper we propose a computational approach that applies data mining techniques to analyze the citizen participation recorded in an online digital platform. Differently to previous work, the approach exploits external knowledge extracted from Open Government Data for processing the citizens’ proposals and debates of the platform, enabling to characterize targeted issues and problems, and analyze

This study aims to reveal the mechanism of how consumers’ engagement is established in social commerce communities. Building upon social support theory, we propose models and employ datasets obtained from Douban.com to empirically investigate the different effects of emotional and informational support on consumers’ engagement and how involvement mediates these relationships in the social commerce

People increasingly use Social Media (SM) platforms such as Twitter and Facebook during disasters and emergencies to post situational updates including reports of injured or dead people, infrastructure damage, requests of urgent needs, and the like. Information on SM comes in many forms, such as textual messages, images, and videos. Several studies have shown the utility of SM information for disaster

Various e-commerce platforms allow sellers to register, describe and organize their own products, using tags and other textual metadata. The quality of these textual descriptors is essential for the effectiveness of e-commerce information services such as search and product recommendation, and thus, for the ability of consumers to find desired products. In this paper, we focus on a particular, widely

Rare behaviors indicate important events and situations in maritime surveillance applications. State-of-the-art methods provide many effective solutions to detect anomalous behaviors. Meanwhile, most solutions are parameter-laden and too costly to identify useful rare behaviors with human knowledge in a visual analytics manner. This paper is concerned with a scheme cross trajectories, vessel attributes

In this work, we propose a method for the automated refinement of subject annotations in biomedical literature at the level of concepts. Semantic indexing and search of biomedical articles in MEDLINE/PubMed are based on semantic subject annotations with MeSH descriptors that may correspond to several related but distinct biomedical concepts. Such semantic annotations do not adhere to the level of detail

Studies of knowledge communities have focused predominantly on contributors who ask questions and/or post replies, while little research has examined the contributions of those who neither pose questions nor suggest answers in knowledge communities. To illuminate member contributions of various sorts, this study evaluated user contribution to knowledge community from three dimensions (influence, content-contribution

Proper parameters can improve performance of entropy methods for discerning electrocardiogram (ECG) signals. So, we tried to determine proper parameters of three entropy methods i.e., a novel permutation ratio entropy (PRE), sample entropy (SmpE) and permutation entropy (PE) for discerning several typical ECG RR interval recordings i.e., normal sinus rhythm (NSR), congestive heart failure (CHF) and

Online health interactions (OHIs) can benefit patients, physicians, and society. However, little research has been conducted that studies the social value of OHIs for third-party patients who view previous OHIs concerning similar health issues to theirs. Drawing on the literature on social support and information uncertainty, this study established a theoretical model to explore the roles of treatment

We investigate a novel perspective to the development of effective algorithms for contact recommendation in social networks, where the problem consists of automatically predicting people that a given user may wish or benefit from connecting to in the network. Specifically, we explore the connection between contact recommendation and the text information retrieval (IR) task, by investigating the adaptation

Merging the citation counts of arXiv-deposited e-prints (arXiv version) with those of their corresponding published journal articles (publisher version) is an important issue in citation analysis. Using examples of arXiv-deposited e-prints, this article adopts a manual approach to investigate the processing methods used by bibliographic repositories such as Google Scholar, Web of Science, Scopus, Astrophysics

The task of enriching cross-lingual links is to find articles in different languages but representing the same real-world object between multilingual Wikis. In this paper, we propose a novel Multi-Modal Semantic Matching approach, called MMSM, to enrich cross-lingual links for online Wikis. Specifically, MMSM jointly trains two novel end-to-end neural matching models, Entity Description Matching Model

With the widespread application of 3D capture devices, diverse 3D object datasets from different domains have emerged recently. Consequently, how to obtain the 3D objects from different domains is becoming a significant and challenging task. The existing approaches mainly focus on the task of retrieval from the identical dataset, which significantly constrains their implementation in real-world applications

This paper will present a new computing methodology based on the application of cognitive information systems in transformative computing tasks. This methodology is an innovative approach to the tasks of transformative computing, which – based on the techniques of collection of data obtained from various sources and registered by means of various sensors – serve the purpose of their appropriate classification

Graph neural networks (GNNs) have shown great potential for personalized recommendation. At the core is to reorganize interaction data as a user-item bipartite graph and exploit high-order connectivity among user and item nodes to enrich their representations. While achieving great success, most existing works consider interaction graph based only on ID information, foregoing item contents from multiple

We calculated the matching values of technology supply and demand texts based on texts semantic similarity with Word2Vec and Cosine similarity algorithms, and then proposed a new index named Supply-Demand Matching Efficiency (SDME) to measure the matching efficiency of online technology trading platforms (OTTPs). Through the empirical research on the three types of OTTPs, the findings are as follows:

Collaborative frequent itemset mining involves analyzing the data shared from multiple business entities to find interesting patterns from it. However, this comes at the cost of high privacy risk. Because some of these patterns may contain business-sensitive information and hence are denoted as sensitive patterns. The revelation of such patterns can disclose confidential information. Privacy-preserving

As handling fashion big data with Artificial Intelligence (AI) has become exciting challenges for computer scientists, fashion studies have received increasing attention in computer vision, machine learning and multimedia communities in the past few years. In this paper, introduce the progress in fashion research and provide a taxonomy of these fashion studies that include low-level fashion recognition

Identifying perceived emotional content of music constitutes an important aspect of easy and efficient search, retrieval, and management of the media. One of the most promising use cases of music organization is an emotion-based playlist, where automatic music emotion recognition plays a significant role in providing emotion related information, which is otherwise, generally unavailable. Based on the