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专线网络的出现,是为了专线网络用户数据信息在传输时更为安全,价格相对昂贵的专线网络是如何提高数据信息传输的安全性呢,本文从专线网络的社会需求、系统构建的必要、构建理念等方面阐述了如何建立专线网络的信息安全保障系统。 相似文献
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《Information processing & management》2023,60(5):103450
Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource languages. This work focuses on providing a novel method for abusive content detection in multiple low-resource Indic languages. Our observation indicates that a post’s tendency to attract abusive comments, as well as features such as user history and social context, significantly aid in the detection of abusive content. The proposed method first learns social and text context features in two separate modules. The integrated representation from these modules is learned and used for the final prediction. To evaluate the performance of our method against different classical and state-of-the-art methods, we have performed extensive experiments on SCIDN and MACI datasets consisting of 1.5M and 665K multilingual comments, respectively. Our proposed method outperforms state-of-the-art baseline methods with an average increase of 4.08% and 9.52% in the F1-score on SCIDN and MACI datasets, respectively. 相似文献
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《Information processing & management》2023,60(3):103277
Warning: This paper contains abusive samples that may cause discomfort to readers.Abusive language on social media reinforces prejudice against an individual or a specific group of people, which greatly hampers freedom of expression. With the rise of large-scale pre-trained language models, classification based on pre-trained language models has gradually become a paradigm for automatic abusive language detection. However, the effect of stereotypes inherent in language models on the detection of abusive language remains unknown, although this may further reinforce biases against the minorities. To this end, in this paper, we use multiple metrics to measure the presence of bias in language models and analyze the impact of these inherent biases in automatic abusive language detection. On the basis of this quantitative analysis, we propose two different debiasing strategies, token debiasing and sentence debiasing, which are jointly applied to reduce the bias of language models in abusive language detection without degrading the classification performance. Specifically, for the token debiasing strategy, we reduce the discrimination of the language model against protected attribute terms of a certain group by random probability estimation. For the sentence debiasing strategy, we replace protected attribute terms and augment the original text by counterfactual augmentation to obtain debiased samples, and use the consistency regularization between the original data and the augmented samples to eliminate the bias at the sentence level of the language model. The experimental results confirm that our method can not only reduce the bias of the language model in the abusive language detection task, but also effectively improve the performance of abusive language detection. 相似文献
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Aspect-based sentiment analysis aims to predict the sentiment polarities of specific targets in a given text. Recent researches show great interest in modeling the target and context with attention network to obtain more effective feature representation for sentiment classification task. However, the use of an average vector of target for computing the attention score for context is unfair. Besides, the interaction mechanism is simple thus need to be further improved. To solve the above problems, this paper first proposes a coattention mechanism which models both target-level and context-level attention alternatively so as to focus on those key words of targets to learn more effective context representation. On this basis, we implement a Coattention-LSTM network which learns nonlinear representations of context and target simultaneously and can extracts more effective sentiment feature from coattention mechanism. Further, a Coattention-MemNet network which adopts a multiple-hops coattention mechanism is proposed to improve the sentiment classification result. Finally, we propose a new location weighted function which considers the location information to enhance the performance of coattention mechanism. Extensive experiments on two public datasets demonstrate the effectiveness of all proposed methods, and our findings in the experiments provide new insight for future developments of using attention mechanism and deep neural network for aspect-based sentiment analysis. 相似文献
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Shiwei Zhang Xiuzhen Zhang Jeffrey Chan Paolo Rosso 《Information processing & management》2019,56(5):1633-1644
Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection. 相似文献
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《Information processing & management》2023,60(4):103381
Social networks have grown into a widespread form of communication that allows a large number of users to participate in conversations and consume information at any time. The casual nature of social media allows for nonstandard terminology, some of which may be considered rude and derogatory. As a result, a significant portion of social media users is found to express disrespectful language. This problem may intensify in certain developing countries where young children are granted unsupervised access to social media platforms. Furthermore, the sheer amount of social media data generated daily by millions of users makes it impractical for humans to monitor and regulate inappropriate content. If adolescents are exposed to these harmful language patterns without adequate supervision, they may feel obliged to adopt them. In addition, unrestricted aggression in online forums may result in cyberbullying and other dreadful occurrences. While computational linguistics research has addressed the difficulty of detecting abusive dialogues, issues remain unanswered for low-resource languages with little annotated data, leading the majority of supervised techniques to perform poorly. In addition, social media content is often presented in complex, context-rich formats that encourage creative user involvement. Therefore, we propose to improve the performance of abusive language detection and classification in a low-resource setting, using both the abundant unlabeled data and the context features via the co-training protocol that enables two machine learning models, each learning from an orthogonal set of features, to teach each other, resulting in an overall performance improvement. Empirical results reveal that our proposed framework achieves F1 values of 0.922 and 0.827, surpassing the state-of-the-art baselines by 3.32% and 45.85% for binary and fine-grained classification tasks, respectively. In addition to proving the efficacy of co-training in a low-resource situation for abusive language detection and classification tasks, the findings shed light on several opportunities to use unlabeled data and contextual characteristics of social networks in a variety of social computing applications. 相似文献
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Minh-Tien Nguyen Viet Cuong Tran Xuan Hoai Nguyen Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515
In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods. 相似文献
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全面利用专利无效诉讼中的专利信息分析产业竞争态势及策略,对于提升企业竞争决策效率具有实践意义。以我国信息通信(ICT)产业的发明专利无效数据为基础,运用社会网络分析方法构建专利无效关系网络,通过网络中心性因子分析和聚类分析将无效诉讼关联主体聚成四类,分析比较各类主体的诉讼地位及特点,挖掘产业竞争的特点并进一步提出具有针对性的无效诉讼策略。研究发现我国ICT产业的竞争格局分化较为严重,表现出"偶然"及"离散"性特点。最后针对每一种类型企业提出其竞争策略构建意见。 相似文献
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基于社会网络和知识基础理论,以知识管理能力为中介变量,并打开其“黑箱”,探究企业社会网络影响创新绩效的内在机理,运用结构方程模型分析和Bootstrap等方法,对相关假设进行检验。结果表明,企业内部社会网络能够通过内部知识管理能力的中介作用提升企业创新绩效,内部社会网络会依次通过知识创造、知识转化和知识创新能力的链式传导作用,对企业创新绩效产生积极影响;企业外部社会网络能够通过外部知识管理能力的中介作用提升企业创新绩效,外部社会网络会依次通过知识吸收、知识连接和知识解吸能力的链式传导作用,对企业创新绩效产生积极影响;企业内、外部社会网络的交互项也能够通过内部和外部知识管理能力的中介作用提升企业创新绩效。 相似文献
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Fattane Zarrinkalam Mohsen Kahani Ebrahim Bagheri 《Information processing & management》2018,54(2):339-357
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users. 相似文献
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对国内外社会网络视角下的知识管理研究状况进行了系统归纳和总结,指出目前社会网络与知识管理的结合研究主要围绕社会网络视角下的企业知识活动形成机制、社会网络属性对知识管理中的知识活动着重研究社会网络、知识管理如何共同提升企业竞争力,社会网络、知识管理、竞争情报的交融以及企业知识网络等问题. 相似文献
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网络舆情是社会舆情的重要组成部分,更是党和政府治国理政、领导干部了解社情民意的重要平台。本文在分析网络舆情演变规律的基础上,以建立完善组织保障体系、畅通的信息公开机制、舆论引导机制等为重点,提出应对群体性突发事件网络舆情的对策与建议。 相似文献
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近年来“网络泄愤”现象逐渐引起了社会的广泛关注,文章运用网络民族志的方法对C地城管打人这一个案进行研究。网络泄愤具有参与地点的虚拟性、表达方式的符号性、泄愤方式的暴力性与信息来源的权威性四个核心特征。转型期社会矛盾的“溢出效应”与网络空间的公共性是网络泄愤出现的主要原因。网络泄愤分为动员型与非动员型两种形态,前者包括人肉搜索与围观、恶语煽动、网络口号;后者包括辱骂性言辞、关联性叙述与戏谑性反讽。从社会稳定、司法公正、个人权利三个方面探讨了网络泄愤的限度与政府治理问题。 相似文献
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【目的/意义】微博是公共图书馆进行社会推广、业界交流、用户交互的重要渠道,从社会网络视角分析公共图书馆微博意见领袖的社会网络结构特点及影响力,可为公共图书馆优化微博营销策略、提高自身影响力提供参考。【方法/过程】选取50位公共图书馆微博意见领袖,首先运用社会网络分析方法揭示其社会网络结构特点;其次利用关注量、发文量、粉丝量、转评赞数量分析其活跃情况及影响力。【结果/结论】公共图书馆微博意见领袖地区分布不均衡,联系较紧密,但集中程度较弱;小团体在活跃度、影响力等方面呈现相似性;多数图书馆处于低活跃度、低影响力区间;粉丝量和转评赞数量随着活跃度的提升表现出“低值时平稳波动,高值时迅速增长”的现象。【创新/局限】通过社会网络分析方法在一定程度上掌握了我国公共图书馆微博意见领袖社会结构及影响力。仅从关注量、发文量等客观数据角度分析公共图书馆微博影响力,未来应结合文本分析等方法提高影响力分析的深度。 相似文献
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[目的/意义]从社会网络视角构建图书馆理想的学术网络,促进图书馆学术研究,从而提高图书馆员知识服务的能力。[方法/过程]通过8种图书情报专业期刊的载文分析图书馆当前的学术生态环境,对比自然科学的学术网络结构,总结出图书情报学常见的网络结构,以网络结构洞理论的视角结合知识图谱对机构、研究主题、作者学术网络作全面的解读。[结果/结论]设置合理“K-丛”值约束学术凝聚子群网络,配合相应的科研政策,不仅适合图书馆的学术科研发展,也适用于整个图书情报学的良性发展。 相似文献
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[目的/意义] 随着我国经济的快速发展,国民对健康管理及医疗质量提出了更高的要求,智慧医疗服务模式已成为我国医疗领域发展的重要趋势。本文对信息交互行为的过程进行了深入剖析,对于该方向的后续研究和发展提供了参考。[方法/过程] 利用文献研究法、定性分析法对智慧医疗情境下信息交互行为的组成要素及交互过程进行分析,运用社会网络分析法对智慧医疗情境下用户信息交互的网络拓扑结构进行了探索性探究。[结果/结论] 智慧医疗情境下信息交互的整个过程会受到来自用户、信息、媒介、技术与环境等因素的影响,在稳定、有序的社会网络结构支撑下,信息交互行为才得以发挥其最大效能。 相似文献