-
Liu, M.; Bu, Y.; Chen, C.; Xu, J.; Li, D.; Leng, Y.; Freeman, R.B.; Meyer, E.T.; Yoon, W.; Sung, M.; Jeong, M.; Lee, J.; Kang, J.; Min, C.; Zhai, Y.; Song, M.; Ding, Y.: Pandemics are catalysts of scientific novelty : evidence from COVID-19 (2022)
0.00
0.0037558496 = product of:
0.011267548 = sum of:
0.0037603125 = weight(_text_:e in 633) [ClassicSimilarity], result of:
0.0037603125 = score(doc=633,freq=2.0), product of:
0.047356583 = queryWeight, product of:
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.03294669 = queryNorm
0.07940422 = fieldWeight in 633, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.0390625 = fieldNorm(doc=633)
0.007507236 = product of:
0.022521708 = sum of:
0.022521708 = weight(_text_:29 in 633) [ClassicSimilarity], result of:
0.022521708 = score(doc=633,freq=2.0), product of:
0.11589616 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.03294669 = queryNorm
0.19432661 = fieldWeight in 633, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.0390625 = fieldNorm(doc=633)
0.33333334 = coord(1/3)
0.33333334 = coord(2/6)
- Abstract
- Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.
- Language
- e
-
Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011)
0.00
8.863142E-4 = product of:
0.005317885 = sum of:
0.005317885 = weight(_text_:e in 4759) [ClassicSimilarity], result of:
0.005317885 = score(doc=4759,freq=4.0), product of:
0.047356583 = queryWeight, product of:
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.03294669 = queryNorm
0.112294525 = fieldWeight in 4759, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.0390625 = fieldNorm(doc=4759)
0.16666667 = coord(1/6)
- Language
- e
-
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
8.863142E-4 = product of:
0.005317885 = sum of:
0.005317885 = weight(_text_:e in 4970) [ClassicSimilarity], result of:
0.005317885 = score(doc=4970,freq=4.0), product of:
0.047356583 = queryWeight, product of:
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.03294669 = queryNorm
0.112294525 = fieldWeight in 4970, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.0390625 = fieldNorm(doc=4970)
0.16666667 = coord(1/6)
- 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.
- Language
- e
-
Li, D.; Wang, Y.; Madden, A.; Ding, Y.; Sun, G.G.; Zhang, N.; Zhou, E.: Analyzing stock market trends using social media user moods and social influence (2019)
0.00
8.863142E-4 = product of:
0.005317885 = sum of:
0.005317885 = weight(_text_:e in 5362) [ClassicSimilarity], result of:
0.005317885 = score(doc=5362,freq=4.0), product of:
0.047356583 = queryWeight, product of:
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.03294669 = queryNorm
0.112294525 = fieldWeight in 5362, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.0390625 = fieldNorm(doc=5362)
0.16666667 = coord(1/6)
- Language
- e
-
Sugimoto, C.R.; Li, D.; Russell, T.G.; Finlay, S.C.; Ding, Y.: ¬The shifting sands of disciplinary development : analyzing North American Library and Information Science dissertations using latent Dirichlet allocation (2011)
0.00
6.2671874E-4 = product of:
0.0037603125 = sum of:
0.0037603125 = weight(_text_:e in 4143) [ClassicSimilarity], result of:
0.0037603125 = score(doc=4143,freq=2.0), product of:
0.047356583 = queryWeight, product of:
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.03294669 = queryNorm
0.07940422 = fieldWeight in 4143, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.0390625 = fieldNorm(doc=4143)
0.16666667 = coord(1/6)
- Language
- e
-
Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015)
0.00
6.2671874E-4 = product of:
0.0037603125 = sum of:
0.0037603125 = weight(_text_:e in 2345) [ClassicSimilarity], result of:
0.0037603125 = score(doc=2345,freq=2.0), product of:
0.047356583 = queryWeight, product of:
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.03294669 = queryNorm
0.07940422 = fieldWeight in 2345, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.0390625 = fieldNorm(doc=2345)
0.16666667 = coord(1/6)
- Language
- e
-
Li, D.; Luo, Z.; Ding, Y.; Tang, J.; Sun, G.G.-Z.; Dai, X.; Du, J.; Zhang, J.; Kong, S.: User-level microblogging recommendation incorporating social influence (2017)
0.00
6.2671874E-4 = product of:
0.0037603125 = sum of:
0.0037603125 = weight(_text_:e in 3426) [ClassicSimilarity], result of:
0.0037603125 = score(doc=3426,freq=2.0), product of:
0.047356583 = queryWeight, product of:
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.03294669 = queryNorm
0.07940422 = fieldWeight in 3426, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.43737 = idf(docFreq=28552, maxDocs=44218)
0.0390625 = fieldNorm(doc=3426)
0.16666667 = coord(1/6)
- Language
- e
