Users’ ability to perceive misinformation: An information quality assessment approach

Digital information exchange enables quick creation and sharing of information and thus changes existing habits. Social media is becoming the main source of news for end-users replacing traditional media. This also enables the proliferation of fake news, which misinforms readers and is used to serve the interests of the creators. As a result, automated fake news detection systems are attracting attention. However, automatic fake news detection presents a major challenge; content evaluation is increasingly becoming the responsibility of the end-user. Thus, in the present study we used information quality (IQ) as an instrument to investigate how users can detect fake news. Specifically, we examined how users perceive fake news in the form of shorter paragraphs on individual IQ dimensions. We also investigated which user characteristics might affect fake news detection. We performed an empirical study with 1123 users, who evaluated randomly generated stories with statements of various level of correctness by individual IQ dimensions. The results reveal that IQ can be used as a tool for fake news detection. Our findings show that (1) domain knowledge has a positive impact on fake news detection; (2) education in combination with domain knowledge improves fake news detection; and (3) personality trait conscientiousness contributes significantly to fake news detection in all dimensions.     

Reviewer assignment algorithms for peer review automation: A survey

Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers.     

Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic features

When cybercriminals communicate with their customers in underground markets, they tend to use secure and customizable instant messaging (IM) software, i.e. Telegram. It is a popular IM software with over 700 million monthly active users (MAU) up to June 2022. In recent years, more and more dark jargons (i.e. an innocent-looking replacement of sensitive terms) appear frequently on Telegram. Therefore, jargons identification is one of the most significant research perspectives to track online underground markets and cybercrimes. This paper proposes a novel Chinese Jargons Identification Framework (CJI-Framework) to identify dark jargons. Firstly, we collect chat history from Telegram groups that are related to the underground market and construct the corpus TUMCC (Telegram Underground Market Chinese Corpus), which is the first Chinese corpus in jargons identification research field. Secondly, we extract seven brand-new features which can be classified into three categories: Vectors-based Features (VF), Lexical analysis-based Features (LF), and Dictionary analysis-based Features (DF), to identify Chinese dark jargons from commonly-used words. Based on these features, we then run a statistical outlier detection to decide whether a word is a jargon. Furthermore, we employ a word vector projection method and a transfer learning method to improve the effect of the framework. Experimental results show that CJI-Framework achieves a remarkable performance with an F1-score of 89.66%. After adaptation for English, it performs better than state-of-the-art English jargons identification method as well. Our built corpus and code have been publicly released to facilitate the reproduction and extension of our work.     

Improving zero-shot retrieval using dense external expansion

Pseudo-relevance feedback (PRF) is a classical technique to improve search engine retrieval effectiveness, by closing the vocabulary gap between users’ query formulations and the relevant documents. While PRF is typically applied on the same target corpus as the final retrieval, in the past, external expansion techniques have sometimes been applied to obtain a high-quality pseudo-relevant feedback set using the external corpus. However, such external expansion approaches have only been studied for sparse (BoW) retrieval methods, and its effectiveness for recent dense retrieval methods remains under-investigated. Indeed, dense retrieval approaches such as ANCE and ColBERT, which conduct similarity search based on encoded contextualised query and document embeddings, are of increasing importance. Moreover, pseudo-relevance feedback mechanisms have been proposed to further enhance dense retrieval effectiveness. In particular, in this work, we examine the application of dense external expansion to improve zero-shot retrieval effectiveness, i.e. evaluation on corpora without further training. Zero-shot retrieval experiments with six datasets, including two TREC datasets and four BEIR datasets, when applying the MSMARCO passage collection as external corpus, indicate that obtaining external feedback documents using ColBERT can significantly improve NDCG@10 for the sparse retrieval (by upto 28%) and the dense retrieval (by upto 12%). In addition, using ANCE on the external corpus brings upto 30% NDCG@10 improvements for the sparse retrieval and upto 29% for the dense retrieval.     

组态视角下平台企业商业模式创新路径研究

为探索多重条件并发对平台企业商业模式创新的影响,基于机会资源一体观构建商业模式创新路径模型,通过对193个样本进行模糊集定性比较分析发现：平台企业商业模式创新路径形成依赖机会开发和资源联动层面要素的组合;机会开发和资源联动层面不同要素的组合形成两类商业模式创新路径,类型1强调模仿型机会开发与资源联动策略组合,类型2强调创新型机会开发与资源联动策略组合;机会开发适配的资源联动方式存在差异,其中模仿型机会开发与资源共享和资源编排匹配良好,而创新型机会开发与资源编排、资源拓展匹配良好。研究结论揭示了平台企业商业模式创新多条等效路径,丰富了商业模式创新前因研究,也为平台企业商业模式创新实践提供了理论指导。     

Information resources estimation for accurate distribution-based concept drift detection

Machine learning applications must continually utilize label information from the data stream to detect concept drift and adapt to the dynamic behavior. Due to the computational expensiveness of label information, it is impractical to assume that the data stream is fully labeled. Therefore, much research focusing on semi-supervised concept drift detection has been proposed. Despite the large research effort in the literature, there is a lack of analysis on the information resources required with the achievable concept drift detection accuracy. Hence, this paper aims to answer the unexplored research question of “How many labeled samples are required to detect concept drift accurately?” by proposing an analytical framework to analyze and estimate the information resources required to detect concept drift accurately. Specifically, this paper disintegrates the distribution-based concept drift detection task into a learning task and a dissimilarity measurement task for independent analyses. The analyses results are then correlated to estimate the required number of labels within a set of data samples to detect concept drift accurately. The proximity of the information resources estimation is evaluated empirically, where the results suggest that the estimation is accurate with high amount of information resources provided. Additionally, estimation results of a state-of-the-art method and a benchmark data set are reported to show the applicability of the estimation by proposed analytical framework within benchmarked environments. In general, the estimation from the proposed analytical framework can serve as guidance in designing systems with limited information resources. This paper also hopes to assist in identifying research gaps and inspiring new research ideas regarding the analysis of the amount of information resources required for accurate concept drift detection.     

Understanding information diffusion with psychological field dynamic

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.     

出口技术演进的减排特征：赶超、扭曲与资源利用效率

   过去的几十年,中国通过支持偏离自身比较优势产业的发展使本国出口技术复杂度得到了快速提升,但这种发展同时伴随着政府的市场干预和较低的资源利用效率。我们以政府干预为切入点,利用STIRPAT模型并结合GMM-System估计方法研究发现：在排除市场扭曲干扰时,出口技术“适应性”偏离比较优势的赶超有助于提高资源利用效率,从而降低碳排放强度;考虑市场扭曲时,出口技术赶超的减排效应发生重要改变,具体地,市场扭曲使得出口技术较低程度的赶超不利于经济发展水平落后和资源要素富集地区的减排,只有当这种赶超在较大距离上偏离比较优势时则会扭转消极影响,但目前并未实现;市场扭曲抑制了经济发展水平较高和资源匮乏地区的资源、劳动密集型行业出口技术赶超对资源利用效率的积极效应。     

碳市场建设对资源型城市资源依赖度的影响研究——基于异期DID模型

本文以降低资源依赖度作为切入点,以中国资源型城市为样本,鉴于不同碳交易试点地区开展试点工作的时间不一致,采用异期双重差分法（DID）考察碳交易市场试点对资源型城市资源依赖度的影响。实证结果发现：碳交易能够较为显著地降低资源型城市对传统资源的依赖程度,但此种影响具有一定滞后性,且平行趋势检验和安慰剂检验结果均能够支持这一实证结果。本研究结论对于增强中国碳交易市场发展信心、促进区域经济绿色低碳转型有一定的积极作用。     

柔性人力资源管理对企业双元创新的影响：环境竞争性的调节作用

本文研究了柔性人力资源管理(含资源柔性人力资源管理与协调柔性人力资源管理)、环境竞争性和双元创新之间的关系。以235家高新技术企业为实证样本,结果表明：资源柔性人力资源管理、协调柔性人力资源管理均能够促进双元创新;与协调柔性人力资源管理相比,资源柔性人力资源管理对双元创新的促进作用更强。环境竞争性能够正向调节资源柔性人力资源管理与双元创新之间的关系,而环境竞争性仅正向调节协调柔性人力资源管理与突破性创新之间的关系。