查看更多>>摘要：Sharing of misinformation on social media platforms is a global concern, with research offering little insight into the motives behind such sharing. Drawing from the cognitive load theory and literature on cognitive ability, we developed and tested a research model hypothesising why people share misinformation. We also tested the moderating role of cognitive ability. We obtained data from 385 social media users in Nigeria using a chain referral technique with an online questionnaire as the instrument for data collection. Our findings suggest that information overload and social media fatigue are strong predictors of misinformation sharing. Information stress also contributed to misinformation sharing behaviour. Furthermore, cognitive ability moderated and weakened the effect information strain and information overload have on misinformation sharing in such a way that this effect is more pronounced among those with low cognitive ability. This indicates that those with low cognitive ability have a higher tendency to share misinformation. However, cognitive ability had no effect on the effect social media fatigue has on misinformation sharing behaviour. The study concluded with some theoretical and practical implications.
查看更多>>摘要：Extant literature on measuring the performance of physicians' knowledge contribution in an online health community (OHC) is limited. To address this gap, this article aims to (1) develop a measurement model for physicians' knowledge contribution performance; (2) use BP neural network to assign reasonable weight to each indicator of the model; and (3) explore the status and differences of knowledge contribution performance among a group of physicians. Based on the sample of 5407 infectious disease physicians in a Chinese OHC, we propose the measurement model by integrating physicians' active knowledge contribution (AKC) and responsive knowledge contribution (RKC), covering 11 dimensions of contribution quantity and quality. We employ the BP neural network to optimise the model weights using the initial weight of the model obtained by the entropy method. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to evaluate the performance of physicians' knowledge contribution in the OHC. The results show that it is feasible to use BP neural network to assign model weights. The distribution of physicians' knowledge contribution performance is uneven; only a few have a high-level knowledge contribution performance. Meanwhile, a significant positive correlation exists between a physician's title and respective knowledge contribution performance. Our research may contribute to related literature and practices by offering a fine-grained understanding of the performance of physicians' knowledge contribution.
查看更多>>摘要：Data deduplication is process of discovering multiple representations of same entity in an information system. Blocking has been a benchmark technique for avoiding the pair-wise record comparisons in data deduplication. Standard blocking (SB) aims at putting the potential duplicate records in the same block on the basis of a blocking key. Afterwards, the detailed comparisons are made only among the records residing in the same block. The selection of blocking key is a tedious process that involves exponential alternatives. The outcome of SB varies considerably with a change in blocking key. To this end, we have proposed a robust blocking technique called Locality Sensitive Blocking (LSB) that does not require the selection of blocking key. The experimental results show an increase of up to 0.448 in F-score as compared with SB. Furthermore, it is found that LSB is more robust towards blocking parameters and data noise.
查看更多>>摘要：Under the new media environment, social platforms, as the carrier of information propagation, have shown a drastic change in their form and structure, endowing public opinion with unique propagation characteristics. In view of this, considering the dynamic changes of online social network (OSN) structure, this article intends to analyse the spreading mechanism of public opinion in temporal networks and improve the applicability of public opinion governance strategies. Combing the changes of OSN topology with the classical susceptible-infected-recovered (SIR) dynamics model, we constructed a co-evolution model of temporal networks structure and public opinion propagation, and the propagation threshold of public opinion was derived with the help of Markov process. Then, the propagation characteristics of public opinion in temporal networks and their co-evolution process under different factors were discussed through simulation experiments. The results show that the propagation of public opinion in temporal networks has faster speed and wider scope compared with that in static networks; netizens' social activity has a phased impact on the evolution process of public opinion and with its significant heterogeneity, the propagation of public opinion is gradually suppressed; compared with Ignorant, the evolution process of public opinion in temporal networks is more sensitive to the state change of public opinion Spreader. Our research can further enrich the theoretical research system of network science and information science and also provide a certain decision-making reference for the regulators to reasonably govern the propagation of public opinion in social platforms.
查看更多>>摘要：Taking Big Data research as a case study, this article intends to investigate the cognitive relatedness of research topics across the global science landscape to a focal topic. Several levels of cognitive relatedness are established depending on the citation distance between the citing publications and a core set of publications. The concept of citation generation is adopted for identifying and classifying other publications with different levels of relatedness to the core set. The micro publication-level classification system of Centre for Science and Technology Studies (CWTS) is applied for determining clusters of publication sets at the topic level. The overall cognitive relatedness of micro clusters to Big Data core publications are measured based on the mean citation generation of all the publications in corresponding clusters. In addition to the given clusters, this study also explores the 'topics' relatedness from a semantic point of view, by extracting high-frequency title terms of publications in each generation. Results show that data analysis methods and technologies are the topics with the strongest cognitive relatedness to Big Data research, while topics on physics and astronomy studies present the weakest relatedness. This approach allows assessment of relatedness between research topics by considering the citations distribution across multiple citation generations, and can provide useful insights to study and characterise topics with fuzzy boundaries or are difficult to delineate, thus representing a novel toolset relevant in the context of studying interdisciplinary research.
查看更多>>摘要：Sentiment analysis of the text deals with the mining of the opinions of people from their written communication. With the increasing usage of online social media platforms for user interactions, abundant opinionated textual data emerges. Therefore, it leads to increased mining of opinions and sentiments and hence greater interest in sentiment analysis. The article introduces the novel Lexico-Semantic features and their use in the sentiment polarity task of English language text. These features are derived using the semantic extension of the lexicons by employing sentiment lexicons and semantic models. These features make data sample size consistent when used in deep learning settings, thereby eliminating the zero padding. For evaluation, we use different semantic models and lexicons to determine the role and impact of Lexico-Semantic features in classification performance. These features, along with the other features, are used to train the different classifiers. Our experimental evaluation shows that introducing Lexico-Semantic features to various state-of-the-art methods of both machine and deep learning improves the overall performance of classifiers.
Bilal Abu-SalihDana Al QudahMalak Al-HassanSeyed Mohssen Ghafari...
1471-1498页
查看更多>>摘要：The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users' behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules.
Ghasem Ali EhsanianSafiyeh Tahmasebi LimooniMitra Ghiasi
1499-1510页
查看更多>>摘要：The aim of this study is to identify the strategic indicators Internet of Things (IoT) application in libraries and to present a conceptual model. The research was performed qualitatively and the data method of the foundation. Data were collected through documentary methods and interviews with a statistical sample of 13 professors of information science and the snowball sampling method. Interview data were analysed in three steps of open, axial and selective coding. Validity assessment was determined by the responsive method and reliability of two coding tests. After analysing the findings, 35 main codes of subcategories and concepts were discovered. The main categories were divided into eight categories: control and supervision, providing advanced services, accessibility, intelligence, maintaining security, new thinking and development, information literacy and method of use and satisfaction, and based on this, a paradigmatic and theoretical model was presented. In the theoretical model of the main phenomenon, the strategic indicators are the use of IoT in libraries, and the classes and the main phenomenon are related to the main class of application strategies. Strategic indicators of IoT application are leading in the growth and development of libraries. These indicators are important in the IoT use in libraries and their development. The eight categories identified in the conceptual model were considered significant by the interviewees. Thus, the indicators discovered are effective and necessary in the success of libraries that use the IoT.
查看更多>>摘要：The aim of this study was to investigate the hotspots of WeChat official accounts and the impact of their pushes on user information behaviour including reading rate, sharing rate, number of comments or collections and fan growth rate. Using nine official accounts provided by the Sootoo Network, this study collected data on more than 10,000 pushes released from January to December 2017. In this study, a second-order user information behaviour model using the collected data was constructed. Based on empirical research, a prediction model of user information behaviour was built using a backpropagation neural network and random forest algorithm, and two variable sets were used for training. Then, the effect of different prediction models was analysed to determine the main factors affecting user information behaviour. This study addresses gaps in the field of WeChat research, and the results are of great practical significance for the operators of WeChat official accounts: they can help them optimise operation effects and enhance the influence of official accounts.
查看更多>>摘要：This article explores the role of information in high risk consumer decision making. Forty-two qualitative interviews were undertaken with international non-EU postgraduates when making the high risk decision to study in a UK Business School. Prospective international postgraduates moved iteratively through the stages in Kuhlthau's Information Search Process model and learnt from the search process they had undertaken in a continuous cyclical manner. Word-of-mouth recommendations were the most influential sources of information gathered, and online sources were perceived to be credible regardless of their origins. The perception of risk impacted the rigour of the information search process. An iterative decision making cycle model is proposed with Kuhlthau's model and word of mouth information at its core, which reflects the connectedness of individuals in this digital era. This study provides new insights by combining both marketing and LIS models and extends Kuhlthau's research into a new context.
NETL
NSTL
Sage