Chapter 8 The Research Experiment of the Development of Emerging Topic
8.5 Discussion
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Fig. 8-12 The Topic Evolution of Support Vector Over Time from 1994 to 2004 (Jo et al., 2007).
Fig. 8-13 The Detection Point of Support Vector in ACM Database
8.5 Discussion
The NI helps researchers to exam the research topic at the view point of novelty and aging theory. The novelty and aging concepts should be considered while we discuss the emerging topic detection not only the hot topic detection. The emergency should new and urgent.
The PVI differs from past simple frequency line as in the (Jo et al., 2007). It only 0
0.2 0.4 0.6 0.8 1 1.2
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
J-volume J-novelty C-volume C-novelty
Year
C-detection point
J-detection point
T he va lue of d et ec ti on po int
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The combination of the NI and PVI can draw up the detection point and the VDP since the DP means the DP present the NI and PVI is both at the highest value. The DP possess the characteristics both novel and hot it matches the expectation of emerging topics. The most important finding is the study develops the method and indices for the researchers to construct their own field topic detection table and exam the new topic in their own field.
This study collects and compiles the journals, transactions, and conference papers in the ACM digital library. The database includes various journals and conferences related to computer science. This study browses all ACM database of publications of journals and transactions, and conference proceedings. There were 689 journal papers and 5154 conference papers published in 2007. Conferences account for 1791 research topics, and journals for 311 topics; the intersection is 89 research topics. From the research topics intersection ratio, the conferences ratio is almost 5%
and the journals ratio is nearly 29%. Thus, the topics discussed in journals are more convergence and are more divergent in conferences.
Although this study demonstrates the development of the emerging topic detection indices, the YCDP and YJDP can determine the lead relationship. The first DP in the curve, regardless of the CDP or JDP, indicates that it is the maximal value of the NI and PVI. Consequently, we suggest that the first DP is a leading position.
The YDP can help determine which type of papers is in the lead position. This study also proves that the first publication time is not the critical factor when determining the YDP. The NI and PVI affect the DP directly. In total, 87.64% of research topics represent the conference topics leads the development of journals. Although this study uses different research methods and different databases than previous works which only focusing on the leading relationship can generate a similar lead relationship results. The experimental results verify the accuracy and effectiveness of the proposed indices.
This study uses the NI and PVI to develop the emerging topic detection indices.
This study uses the concepts as terms, topics, and candidate research topics to investigate topics, and discusses the CNI and CPVI of conferences and the JNI and JPVI of journals. The DP is produced even though the CDP or JDP creates the YDP and VDP to represent the year and the value which the topic becomes emerging. The YCDP and YJDP can be used to determine which type of papers stand the lead position. The VDP can be used to determine whether to investigate a topic further.
Based on the NI and PVI, one can show that while the published volume for the present year is large, the curve will rise to make the DP in the front stage of lifecycle, and decline to make the DP delay. Based on the NI and PVI and the properties of the
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DP, the emerging topic detection indices can determine whether a research topic is worth further investigation. A high VDP indicates that topic novelty is sufficiently high for further research.
Finally, the emerging topic detection indices detect the DP and obtain the YDP and VDP. By comparing conferences and journals, one can determine which reaches the threshold first. However, if a topic has never been important, the topic DP is useless. The emerging topic detection table can be used to examine whether a topic warrants further research. Each topic has its own value; therefore, the value of researching a topic indicates that the topic has not reached its highest point in its lifecycle. When a topic is hot and mature, the potential worthy for further research will decrease in the future. The VDP is the basis of the detection table.
Even when the published volume is low, the PVI will be high since it compares to itself the total number that can decide the PVI. A high VDP represents a large development space for additional effort. Hence, a high VDP indicates that a topic warrants further investigation. Consequently, when one does not know whether a topic is important or worthy, one can compute its VDP and then compare this value with that in the emerging topic detection table. If the VDP is lower than the baseline, the topic is mature or worthless. Comparatively, if a topic’s lifecycle is never higher than the VDP for the same year in the detection table, it has never garnered popular attention; thus, the topic is worthless.
8.6 Summary
This study addresses the inadequacy of topic detection and tracking to develop a set of novel indices for emerging topic detection. The novelty concept is used in combination with aging theory to develop the novelty index (NI). The published volume index (PVI) is improved from traditional frequency to reflect the growth of a discussion. The DP and YDP help determine the relationship between conference topics and journal topics and how long does it lead ahead. The VDP is created to construct the detection table to determine whether a topic warrant further research.
The NI and PVI can be applied to other fields to determine the new trends, for example, the news or stock price predictions.
Future work will continue to work on the extension of NI and PVI with more
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leading relationship between conferences and journals would differ from those of other disciplines.
TextAnalyst was utilized analyze words used in study titles. Therefore, selected features are restricted to the word dictionary in TextAnalyst software. Namely, words that appear repeatedly in a corpus, but are not included in the dictionary are not located due to software limitations.
The year of the potential development of topics is based on the ACM datasets;
thus, this study only observed published volume and development years in the ACM digital library.