-
Zheng, R.; Li, J.; Chen, H.; Huang, Z.: ¬A framework for authorship identification of online messages : writing-style features and classification techniques (2006)
0.09
0.08517113 = product of:
0.17034227 = sum of:
0.15421765 = weight(_text_:vector in 5276) [ClassicSimilarity], result of:
0.15421765 = score(doc=5276,freq=4.0), product of:
0.30654848 = queryWeight, product of:
6.439392 = idf(docFreq=191, maxDocs=44218)
0.047605187 = queryNorm
0.5030775 = fieldWeight in 5276, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
6.439392 = idf(docFreq=191, maxDocs=44218)
0.0390625 = fieldNorm(doc=5276)
0.016124614 = product of:
0.032249227 = sum of:
0.032249227 = weight(_text_:22 in 5276) [ClassicSimilarity], result of:
0.032249227 = score(doc=5276,freq=2.0), product of:
0.16670525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047605187 = queryNorm
0.19345059 = fieldWeight in 5276, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=5276)
0.5 = coord(1/2)
0.5 = coord(2/4)
- Abstract
- With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.
- Date
- 22. 7.2006 16:14:37
-
Li, J.; Zhang, P.; Cao, J.: External concept support for group support systems through Web mining (2009)
0.04
0.042968635 = product of:
0.17187454 = sum of:
0.17187454 = weight(_text_:space in 2806) [ClassicSimilarity], result of:
0.17187454 = score(doc=2806,freq=8.0), product of:
0.24842183 = queryWeight, product of:
5.2183776 = idf(docFreq=650, maxDocs=44218)
0.047605187 = queryNorm
0.6918657 = fieldWeight in 2806, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
5.2183776 = idf(docFreq=650, maxDocs=44218)
0.046875 = fieldNorm(doc=2806)
0.25 = coord(1/4)
- Abstract
- External information plays an important role in group decision-making processes, yet research about external information support for Group Support Systems (GSS) has been lacking. In this study, we propose an approach to build a concept space to provide external concept support for GSS users. Built on a Web mining algorithm, the approach can mine a concept space from the Web and retrieve related concepts from the concept space based on users' comments in a real-time manner. We conduct two experiments to evaluate the quality of the proposed approach and the effectiveness of the external concept support provided by this approach. The experiment results indicate that the concept space mined from the Web contained qualified concepts to stimulate divergent thinking. The results also demonstrate that external concept support in GSS greatly enhanced group productivity for idea generation tasks.
-
Li, J.; Zhang, P.; Song, D.; Wu, Y.: Understanding an enriched multidimensional user relevance model by analyzing query logs (2017)
0.01
0.011665257 = product of:
0.046661027 = sum of:
0.046661027 = product of:
0.09332205 = sum of:
0.09332205 = weight(_text_:model in 3961) [ClassicSimilarity], result of:
0.09332205 = score(doc=3961,freq=8.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.50980973 = fieldWeight in 3961, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.046875 = fieldNorm(doc=3961)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- Modeling multidimensional relevance in information retrieval (IR) has attracted much attention in recent years. However, most existing studies are conducted through relatively small-scale user studies, which may not reflect a real-world and natural search scenario. In this article, we propose to study the multidimensional user relevance model (MURM) on large scale query logs, which record users' various search behaviors (e.g., query reformulations, clicks and dwelling time, etc.) in natural search settings. We advance an existing MURM model (including five dimensions: topicality, novelty, reliability, understandability, and scope) by providing two additional dimensions, that is, interest and habit. The two new dimensions represent personalized relevance judgment on retrieved documents. Further, for each dimension in the enriched MURM model, a set of computable features are formulated. By conducting extensive document ranking experiments on Bing's query logs and TREC session Track data, we systematically investigated the impact of each dimension on retrieval performance and gained a series of insightful findings which may bring benefits for the design of future IR systems.
-
Zhao, S.X.; Zhang, P.L.; Li, J.; Tan, A.M.; Ye, F.Y.: Abstracting the core subnet of weighted networks based on link strengths (2014)
0.01
0.0082485825 = product of:
0.03299433 = sum of:
0.03299433 = product of:
0.06598866 = sum of:
0.06598866 = weight(_text_:model in 1256) [ClassicSimilarity], result of:
0.06598866 = score(doc=1256,freq=4.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.36048993 = fieldWeight in 1256, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.046875 = fieldNorm(doc=1256)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- Most measures of networks are based on the nodes, although links are also elementary units in networks and represent interesting social or physical connections. In this work we suggest an option for exploring networks, called the h-strength, with explicit focus on links and their strengths. The h-strength and its extensions can naturally simplify a complex network to a small and concise subnetwork (h-subnet) but retains the most important links with its core structure. Its applications in 2 typical information networks, the paper cocitation network of a topic (the h-index) and 5 scientific collaboration networks in the field of "water resources," suggest that h-strength and its extensions could be a useful choice for abstracting, simplifying, and visualizing a complex network. Moreover, we observe that the 2 informetric models, the Glänzel-Schubert model and the Hirsch model, roughly hold in the context of the h-strength for the collaboration networks.
-
Xie, Z.; Ouyang, Z.; Li, J.; Dong, E.: Modelling transition phenomena of scientific coauthorship networks (2018)
0.01
0.0082485825 = product of:
0.03299433 = sum of:
0.03299433 = product of:
0.06598866 = sum of:
0.06598866 = weight(_text_:model in 4043) [ClassicSimilarity], result of:
0.06598866 = score(doc=4043,freq=4.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.36048993 = fieldWeight in 4043, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.046875 = fieldNorm(doc=4043)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- In a range of scientific coauthorship networks, transitions emerge in degree distribution, in the correlation between degree and local clustering coefficient, etc. The existence of those transitions could be regarded because of the diversity in collaboration behaviors of scientific fields. A growing geometric hypergraph built on a cluster of concentric circles is proposed to model two specific collaboration behaviors, namely the behaviors of research team leaders and those of the other team members. The model successfully predicts the transitions, as well as many common features of coauthorship networks. Particularly, it realizes a process of deriving the complex "scale-free" property from the simple "yes/no" decisions. Moreover, it provides a reasonable explanation for the emergence of transitions with the difference of collaboration behaviors between leaders and other members. The difference emerges in the evolution of research teams, which synthetically addresses several specific factors of generating collaborations, namely the communications between research teams, academic impacts and homophily of authors.
-
Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020)
0.01
0.006873818 = product of:
0.027495272 = sum of:
0.027495272 = product of:
0.054990545 = sum of:
0.054990545 = weight(_text_:model in 5816) [ClassicSimilarity], result of:
0.054990545 = score(doc=5816,freq=4.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.30040827 = fieldWeight in 5816, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.0390625 = fieldNorm(doc=5816)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word-level indicative representation and message-level indicative representation hierarchically. In both of the modules, we apply character-level representations, which enables the model to explore morphological features and deal with the out-of-vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real-life data sets indicate that our model outperforms state-of-the-art models from previous studies.
-
Shi, D.; Rousseau, R.; Yang, L.; Li, J.: ¬A journal's impact factor is influenced by changes in publication delays of citing journals (2017)
0.01
0.0058326283 = product of:
0.023330513 = sum of:
0.023330513 = product of:
0.046661027 = sum of:
0.046661027 = weight(_text_:model in 3441) [ClassicSimilarity], result of:
0.046661027 = score(doc=3441,freq=2.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.25490487 = fieldWeight in 3441, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.046875 = fieldNorm(doc=3441)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- In this article we describe another problem with journal impact factors by showing that one journal's impact factor is dependent on other journals' publication delays. The proposed theoretical model predicts a monotonically decreasing function of the impact factor as a function of publication delay, on condition that the citation curve of the journal is monotone increasing during the publication window used in the calculation of the journal impact factor; otherwise, this function has a reversed U shape. Our findings based on simulations are verified by examining three journals in the information sciences: the Journal of Informetrics, Scientometrics, and the Journal of the Association for Information Science and Technology.
-
Du, Q.; Li, J.; Du, Y.; Wang, G.A.; Fan, W.: Predicting crowdfunding project success based on backers' language preferences (2021)
0.01
0.0058326283 = product of:
0.023330513 = sum of:
0.023330513 = product of:
0.046661027 = sum of:
0.046661027 = weight(_text_:model in 415) [ClassicSimilarity], result of:
0.046661027 = score(doc=415,freq=2.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.25490487 = fieldWeight in 415, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.046875 = fieldNorm(doc=415)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- Project success is critical in the crowdfunding domain. Rather than the existing project-centric prediction methods, we propose a novel backer-centric prediction method. We identify each backer's preferences based on their pledge history and calculate the cosine similarity between backer's preferences and the project as each backer's persuasibility. Finally, we aggregate all the backers' persuasibility to predict project success. To validate our method, we crawled data on 183,886 projects launched during or before December 2014 on Kickstarter, a crowdfunding website. We selected 4,922 backers with a total of 442,793 pledges to identify backers' preferences. The results show that a backer is more likely to be persuaded by a project that is more similar to the backer's preferences. Our findings not only demonstrate the efficacy of backers' pledge history for predicting crowdfunding project success but also verify that a backer-centric method can supplement the existing project-centric approaches. Our model and findings enable crowdfunding platform agencies, fund-seeking entrepreneurs, and investors to predict the success of a crowdfunding project.
-
Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015)
0.01
0.005700912 = product of:
0.022803647 = sum of:
0.022803647 = product of:
0.045607295 = sum of:
0.045607295 = weight(_text_:22 in 2590) [ClassicSimilarity], result of:
0.045607295 = score(doc=2590,freq=4.0), product of:
0.16670525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047605187 = queryNorm
0.27358043 = fieldWeight in 2590, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=2590)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 20. 1.2015 18:30:22
17. 9.2018 18:22:23
-
Lin, X.; Li, J.; Zhou, X.: Theme creation for digital collections (2008)
0.01
0.0056436146 = product of:
0.022574458 = sum of:
0.022574458 = product of:
0.045148917 = sum of:
0.045148917 = weight(_text_:22 in 2635) [ClassicSimilarity], result of:
0.045148917 = score(doc=2635,freq=2.0), product of:
0.16670525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047605187 = queryNorm
0.2708308 = fieldWeight in 2635, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0546875 = fieldNorm(doc=2635)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Source
- Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
-
Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012)
0.00
0.0048605236 = product of:
0.019442094 = sum of:
0.019442094 = product of:
0.03888419 = sum of:
0.03888419 = weight(_text_:model in 4970) [ClassicSimilarity], result of:
0.03888419 = score(doc=4970,freq=2.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.21242073 = fieldWeight in 4970, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.0390625 = fieldNorm(doc=4970)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.
-
Min, C.; Ding, Y.; Li, J.; Bu, Y.; Pei, L.; Sun, J.: Innovation or imitation : the diffusion of citations (2018)
0.00
0.0048605236 = product of:
0.019442094 = sum of:
0.019442094 = product of:
0.03888419 = sum of:
0.03888419 = weight(_text_:model in 4445) [ClassicSimilarity], result of:
0.03888419 = score(doc=4445,freq=2.0), product of:
0.1830527 = queryWeight, product of:
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.047605187 = queryNorm
0.21242073 = fieldWeight in 4445, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.845226 = idf(docFreq=2569, maxDocs=44218)
0.0390625 = fieldNorm(doc=4445)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Abstract
- Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.
-
Li, J.; Shi, D.: Sleeping beauties in genius work : when were they awakened? (2016)
0.00
0.004837384 = product of:
0.019349536 = sum of:
0.019349536 = product of:
0.03869907 = sum of:
0.03869907 = weight(_text_:22 in 2647) [ClassicSimilarity], result of:
0.03869907 = score(doc=2647,freq=2.0), product of:
0.16670525 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.047605187 = queryNorm
0.23214069 = fieldWeight in 2647, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=2647)
0.5 = coord(1/2)
0.25 = coord(1/4)
- Date
- 22. 1.2016 14:13:32
