-
Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016)
0.02
0.017226078 = product of:
0.051678233 = sum of:
0.051678233 = product of:
0.077517346 = sum of:
0.047734402 = weight(_text_:network in 3059) [ClassicSimilarity], result of:
0.047734402 = score(doc=3059,freq=2.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.2460165 = fieldWeight in 3059, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.0390625 = fieldNorm(doc=3059)
0.029782942 = weight(_text_:29 in 3059) [ClassicSimilarity], result of:
0.029782942 = score(doc=3059,freq=2.0), product of:
0.15326229 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.043569047 = queryNorm
0.19432661 = fieldWeight in 3059, 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=3059)
0.6666667 = coord(2/3)
0.33333334 = coord(1/3)
- Abstract
- This study proposes a new method to construct and trace the trajectory of conceptual development of a research field by combining main path analysis, citation analysis, and text-mining techniques. Main path analysis, a method used commonly to trace the most critical path in a citation network, helps describe the developmental trajectory of a research field. This study extends the main path analysis method and applies text-mining techniques in the new method, which reflects the trajectory of conceptual development in an academic research field more accurately than citation frequency, which represents only the articles examined. Articles can be merged based on similarity of concepts, and by merging concepts the history of a research field can be described more precisely. The new method was applied to the "h-index" and "text mining" fields. The precision, recall, and F-measures of the h-index were 0.738, 0.652, and 0.658 and those of text-mining were 0.501, 0.653, and 0.551, respectively. Last, this study not only establishes the conceptual trajectory map of a research field, but also recommends keywords that are more precise than those used currently by researchers. These precise keywords could enable researchers to gather related works more quickly than before.
- Date
- 21. 7.2016 19:29:19
-
Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018)
0.01
0.0127291735 = product of:
0.03818752 = sum of:
0.03818752 = product of:
0.114562556 = sum of:
0.114562556 = weight(_text_:network in 4286) [ClassicSimilarity], result of:
0.114562556 = score(doc=4286,freq=8.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.59043956 = fieldWeight in 4286, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.046875 = fieldNorm(doc=4286)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.
-
Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010)
0.01
0.009000885 = product of:
0.027002655 = sum of:
0.027002655 = product of:
0.081007965 = sum of:
0.081007965 = weight(_text_:network in 3327) [ClassicSimilarity], result of:
0.081007965 = score(doc=3327,freq=4.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.41750383 = fieldWeight in 3327, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.046875 = fieldNorm(doc=3327)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- Social network sites (SNSs) such as MySpace and Facebook are important venues for interpersonal communication, especially among youth. One way in which members can communicate is to write public messages on each other's profile, but how is this unusual means of communication used in practice? An analysis of 2,293 public comment exchanges extracted from large samples of U.S. and U.K. MySpace members found them to be relatively rapid, but rarely used for prolonged exchanges. They seem to fulfill two purposes: making initial contact and keeping in touch occasionally such as at birthdays and other important dates. Although about half of the dialogs seem to exchange some gossip, the dialogs seem typically too short to play the role of gossip-based social grooming for typical pairs of Friends, but close Friends may still communicate extensively in SNSs with other methods.
-
Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010)
0.01
0.009000885 = product of:
0.027002655 = sum of:
0.027002655 = product of:
0.081007965 = sum of:
0.081007965 = weight(_text_:network in 3704) [ClassicSimilarity], result of:
0.081007965 = score(doc=3704,freq=4.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.41750383 = fieldWeight in 3704, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.046875 = fieldNorm(doc=3704)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.
-
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)
0.01
0.007500738 = product of:
0.022502214 = sum of:
0.022502214 = product of:
0.06750664 = sum of:
0.06750664 = weight(_text_:network in 2345) [ClassicSimilarity], result of:
0.06750664 = score(doc=2345,freq=4.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.34791988 = fieldWeight in 2345, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.0390625 = fieldNorm(doc=2345)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.
-
Zhang, Z.; Li, Q.; Zeng, D.; Ga, H.: Extracting evolutionary communities in community question answering (2014)
0.01
0.0053038225 = product of:
0.015911467 = sum of:
0.015911467 = product of:
0.047734402 = sum of:
0.047734402 = weight(_text_:network in 1286) [ClassicSimilarity], result of:
0.047734402 = score(doc=1286,freq=2.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.2460165 = fieldWeight in 1286, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.0390625 = fieldNorm(doc=1286)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision-making basis for business.
-
Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data (2011)
0.00
0.004243058 = product of:
0.012729174 = sum of:
0.012729174 = product of:
0.038187522 = sum of:
0.038187522 = weight(_text_:network in 354) [ClassicSimilarity], result of:
0.038187522 = score(doc=354,freq=2.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.1968132 = fieldWeight in 354, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.03125 = fieldNorm(doc=354)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Content
- Inhalt: 1. Introduction 2. Association Rules and Sequential Patterns 3. Supervised Learning 4. Unsupervised Learning 5. Partially Supervised Learning 6. Information Retrieval and Web Search 7. Social Network Analysis 8. Web Crawling 9. Structured Data Extraction: Wrapper Generation 10. Information Integration
-
Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
0.00
0.003971059 = product of:
0.011913176 = sum of:
0.011913176 = product of:
0.035739526 = sum of:
0.035739526 = weight(_text_:29 in 3464) [ClassicSimilarity], result of:
0.035739526 = score(doc=3464,freq=2.0), product of:
0.15326229 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.043569047 = queryNorm
0.23319192 = fieldWeight in 3464, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.046875 = fieldNorm(doc=3464)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Date
- 1. 6.2010 9:29:57
-
Qiu, X.Y.; Srinivasan, P.; Hu, Y.: Supervised learning models to predict firm performance with annual reports : an empirical study (2014)
0.00
0.003971059 = product of:
0.011913176 = sum of:
0.011913176 = product of:
0.035739526 = sum of:
0.035739526 = weight(_text_:29 in 1205) [ClassicSimilarity], result of:
0.035739526 = score(doc=1205,freq=2.0), product of:
0.15326229 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.043569047 = queryNorm
0.23319192 = fieldWeight in 1205, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.046875 = fieldNorm(doc=1205)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Date
- 29. 1.2014 16:46:40
-
Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013)
0.00
0.003309216 = product of:
0.009927647 = sum of:
0.009927647 = product of:
0.029782942 = sum of:
0.029782942 = weight(_text_:29 in 967) [ClassicSimilarity], result of:
0.029782942 = score(doc=967,freq=2.0), product of:
0.15326229 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.043569047 = queryNorm
0.19432661 = fieldWeight in 967, 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=967)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Date
- 25. 6.2013 19:05:29
-
Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017)
0.00
0.003309216 = product of:
0.009927647 = sum of:
0.009927647 = product of:
0.029782942 = sum of:
0.029782942 = weight(_text_:29 in 3682) [ClassicSimilarity], result of:
0.029782942 = score(doc=3682,freq=2.0), product of:
0.15326229 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.043569047 = queryNorm
0.19432661 = fieldWeight in 3682, 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=3682)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Date
- 16.11.2017 14:00:29
-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
0.00
0.003279447 = product of:
0.009838341 = sum of:
0.009838341 = product of:
0.029515022 = sum of:
0.029515022 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
0.029515022 = score(doc=668,freq=2.0), product of:
0.15257138 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.043569047 = queryNorm
0.19345059 = fieldWeight in 668, 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=668)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Date
- 22. 3.2013 19:43:01
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
0.00
0.003279447 = product of:
0.009838341 = sum of:
0.009838341 = product of:
0.029515022 = sum of:
0.029515022 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
0.029515022 = score(doc=1605,freq=2.0), product of:
0.15257138 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.043569047 = queryNorm
0.19345059 = fieldWeight in 1605, 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=1605)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
-
Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
0.00
0.003279447 = product of:
0.009838341 = sum of:
0.009838341 = product of:
0.029515022 = sum of:
0.029515022 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
0.029515022 = score(doc=5011,freq=2.0), product of:
0.15257138 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.043569047 = queryNorm
0.19345059 = fieldWeight in 5011, 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=5011)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Date
- 7. 3.2019 16:32:22
-
Jäger, L.: Von Big Data zu Big Brother (2018)
0.00
0.0026235576 = product of:
0.007870672 = sum of:
0.007870672 = product of:
0.023612017 = sum of:
0.023612017 = weight(_text_:22 in 5234) [ClassicSimilarity], result of:
0.023612017 = score(doc=5234,freq=2.0), product of:
0.15257138 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.043569047 = queryNorm
0.15476047 = fieldWeight in 5234, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03125 = fieldNorm(doc=5234)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Date
- 22. 1.2018 11:33:49
