时序分析相关方法在信息检索研究中的应用（英文）
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摘要
时序分析是自然科学和人文社会科学研究的常用方法,在信息检索领域也被广泛使用。本文采用定性与定量相结合的研究方法回顾并分析了1991至2016年时序分析的相关方法在信息检索领域内的应用情况。本文在Web of Science,EBSCOhost和Pro Quest三个数据库中检索了1991至2016年信息检索领域内使用时序分析相关方法的期刊论文、会议论文和学位论文,共计2240篇。本文采用趋势分析和文本内容分析方法对所获得的文献进行梳理和研究。研究结果显示在信息检索领域中,自2004年起时序分析方法的应用呈现明显上升趋势。时序分析方法包括定性和定量两大类,被广泛应用于传统(如文献计量、图书馆学等)、新兴(如社交媒体、商业智能等)的信息检索相关领域,及交叉学科(如健康信息等)的研究中。信息可视化方法和技术经常采用时序分析的理论和方法,同时也频繁地被用于时序分析的研究中。本文的研究结果将有助于信息检索领域的学者们进一步了解时序分析方法及其应用,并在日后的研究中更好地使用和创新时序分析方法。
Nowadays in the information retrieval field,the changing world evokes the needs of exploring patterns and evolutions of certain objects,as well as predicting their future trends. This phenomenon brings increasing applications of temporal analysis,a means for exploring changes overtime.This study is a survey that examines and investigates the studies applying temporal analysis methods in the information retrieval field.Three databases were selected for retrieving the information retrieval studies applying temporal analysis methods. Various materials were obtained,such as research papers,proceedings,theses,and so on. Trend analysis,natural language processing approach,and manually content analysis were used for data analysis.The findings show that the use of temporal analysis keeps increasing rapidly from 2004. Both qualitative and quantitative analysis methods have been applied for temporal analysis. Information visualization technologies are also frequently employed in these studies. Temporal analysis strengthens the information retrieval studies in traditional and emerging information-retrieval-related areas and relevant cross field research areas.These findings help researchers gain insights into temporal analysis and its applications in information retrieval,which will assist them to use these methods and create new temporal analysis methods for various research topics in their future studies.
Nowadays in the information retrieval field,the changing world evokes the needs of exploring patterns and evolutions of certain objects,as well as predicting their future trends. This phenomenon brings increasing applications of temporal analysis,a means for exploring changes overtime.This study is a survey that examines and investigates the studies applying temporal analysis methods in the information retrieval field.Three databases were selected for retrieving the information retrieval studies applying temporal analysis methods. Various materials were obtained,such as research papers,proceedings,theses,and so on. Trend analysis,natural language processing approach,and manually content analysis were used for data analysis.The findings show that the use of temporal analysis keeps increasing rapidly from 2004. Both qualitative and quantitative analysis methods have been applied for temporal analysis. Information visualization technologies are also frequently employed in these studies. Temporal analysis strengthens the information retrieval studies in traditional and emerging information-retrieval-related areas and relevant cross field research areas.These findings help researchers gain insights into temporal analysis and its applications in information retrieval,which will assist them to use these methods and create new temporal analysis methods for various research topics in their future studies.
引文
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[3] Van Deursen A J A M,Van Dijk J A G M. Internet skill levels increase,but gaps widen:A longitudinal crosssectional analysis(2010-2013)among the Dutch population[J]. Information,Communication&Society,2015,18(7):782-797.
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[12] Collins G,Quan-haase A. Are social media ubiquitous in academic libraries? A longitudinal study of adoption and usage patterns[J]. Journal of Web Librarianship,2014,8(1):48-68.
[13] Radford M L,Connaway L S. Not dead yet! A longitudinal study of query type and ready reference accuracy in live chat and IM reference[J]. Library&Information Science Research,2013,35(1):2-13.
[14] Elo S,Kyngas H. The qualitative content analysis process[J]. Journal of Advanced Nursing,2008,62(1):107-115.
[15] Jansen B. Search log analysis:What it is,what's been done,how to do it[J]. Library&Information Science Research,2006,28(3):407-32.
[16] Ayers J,Althouse B,Allem J,et al. Seasonality in seeking mental health information on Google[J]. American journal of preventive medicine,2013,44(5):520-525.
[17] Xu C,Ma B,Chen X,et al. Social tagging in the scholarly world[J]. Journal of the Association for Information Science and Technology,2013,64(10):2045-2057.
[18] Winter J,Zadpoor A,Dodou D. The expansion of Google Scholar versus Web of Science:A longitudinal study[J]. Scientometrics,2014,98(2):1547-1565.
[19] Hoeber O. A longitudinal study of Hot Map web search[J]. Online Information Review,2013,37(2):252-267.
[20] Efron M. Linear time series models for term weighting in information retrieval[J]. Journal of the Association for Information Science and Technology,2010,61(7):1299-1312.
[21] Joho H,Jatowt A,Blanco R. Temporal information searching behaviour and strategies[J]. Information Processing&Management,2015,51(6):834-850.
[22] Ahn J,Taieb-maimon M,Sopan A,et al. Temporal visualization of social network dynamics:Prototypes for nation of neighbors[C]//Social computing,Behavioral-Cultural Modeling and Prediction,March 29-31,2011,College Park,MD,USA. Heidelberg:Springer Berlin Heidelberg,2011:309-316.
[23] Alonso O,Gertz M,Baeza-yates R. On the value of temporal information in information retrieval[J]. ACM SIGIR Forum,2007,41(2):35-41.
[24] Balatsoukas P,Ruthven I. An eye-tracking approach to the analysis of relevance judgments on the web:The case of google search engine[J]. Journal of the American Society for Information Science and Technology,2012,63(9):1728-1746.
[25] Bertini E,Tatu A,Keim D. Quality metrics in high-dimensional data visualization:An overview and systematization[J]. IEEE Transactions on Visualization and Computer Graphics,2011,17(12):2203-2212.
[26] Laxman S,Sastry P S. A survey of temporal data mining[J]. Sadhana,2006,31(2):173-198.
[27] Lazar J,Wentz B,Almalhem A,et al. A longitudinal study of state government homepage accessibility in Maryland and the role of web page templates for improving accessibility[J]. Government Information Quarterly,2013,30(3):289-299.
[28] Lee W-K. An elaboration likelihood model based longitudinal analysis of attitude change during the process of IT acceptance via education program[J]. Behaviour&Information Technology,2012,31(12):1161-1171.
[29] Matthews M,Tolchinsky P,Blanco R,et al. Searching through time in the New York Times[C/OL]//Proceeding of the 4th Workshop on Human-Computer Interaction and Information Retrieval,Aug 22,2010,New Brunswick, NJ, USA.[2017-03-20]. https://pdfs. semanticscholar. org/5046/277f22163abc9a209b57676ac0181d4294b4.pdf#page=41.
[30] Mccown F,Nelson M L. Recovering a website’s server components from the webinfrastructure[C]//Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries,June 16-20,2008,Pittsburgh,PA,USA. New York,NY,USA:ACM,2008:124-133.
[31] Ahlqvist T,Back A,Heinonen S,et al. Road-Mapping the societal transformation potential of social media[J]. Foresight,2010,12(5):3-26(24).
[32] Kaplan A M,Haenlein M. Users of the world,unite! The challenges and opportunities of Social Media[J].Business Horizons,2010,53(1):59-68.
[33] St Jean B. Devising and implementing a card-sorting technique for a longitudinal investigation of the information behavior of people with Type 2 Diabetes[J]. Library&Information Science Research,2014,36(1):16-26.
[34] Stevenson J A,Zhang J. A temporal analysis of institutional repository research[J]. Scientometrics,2015,105(3):1491-1525.
[35] PUOLAMAKI K,SALOJARVI J,Savia E,et al. Combining eye movements and collaborative filtering for proactive information retrieval[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,August 15-19,2005,Salvador,Brazil. New York,NY,USA:ACM,2005:146-153.
[36] Mei Q,Zhai C. Discovering evolutionary theme patterns from text:An exploration of temporal text mining[C]//Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining,August 21-24,2005,Chicago,IL,USA. New York,NY,USA:ACM,2005:198-207.
[37] Jatowt A,Kanazawa K,Oyama S,et al. Supporting analysis of future-related information in news archives and the web[C]//Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries,June 14-19,2009,Austin,TX,USA. New York,NY,USA:ACM,2009:115-124.
[38] Jatowt A,Au Yeung C. Extracting collective expectations about the future from large text collections[C]//Proceedings of the 20th ACM International Conference on Information and Knowledge Management,October24-28,2011,Glasgow,United Kingdom. New York,NY,USA:ACM,2011:1259-1264.
[39] Borgatti S,Everett M,Freeman L. Ucinet for windows:Software for Social Network Analysis[M]. Lexington:Analytic Technologies,2002:preface.
[40] Hansen D,Shneiderman B,Smith M A. Analyzing social media networks with Node XL:Insights from a connected world[M]. Burlington:Morgan Kaufmann,2010:47.
[41] De Nooy W,Mrvar A,Batagel J V. Exploratory social network analysis with pajek[M]. Cambridge:Cambridge University Press,2011:xxiii.
[42] Pecha O,VANECCEK J. Analysis of the Czech and Hungarian physiology publications 1994-2011[J]. Scientometrics,2015,105(2):991-1003.
[43] Zhuang Y,Du C,Zhang L,et al. Research trends and hotspots in soil erosion from 1932 to 2013:A literature review[J]. Scientometrics,2015,105(2):743-758.
[44] RAIHAK-J,Aula A,Majaranta P,et al. Static visualization of temporal eye-tracking data[C]//Costabile M F,PATERNF. Human-Computer Interaction-INTERACT 2005,September 12-16,2005,Rome,Italy. Heidelberg:Springer Berlin Heidelberg,2005:946-949.
[45] Huang D. Temporal evolution of multi-author papers in basic sciences from 1960 to 2010[J]. Scientometrics,2015,105(3):2137-2147.
[46] Huang M-H,Chen S-H,Lin C-Y,et al. Exploring temporal relationships between scientific and technical fronts:A case of biotechnology field[J]. Scientometrics,2013,98(2):1085-1100.
[47] Cheng X,Dale C,Liu J. Statistics and social network of You Tube videos[C]//The 16th International Workshop on Quality of Service,June 2-4,2008,Enschede,The Netherlands. IEEE,2008:229-238.
[48] Peltonen J,Lin Z. Information retrieval approach to meta-visualization[J]. Machine Learning,2014,99(2):189-229.
[49] H9lscher C,Strube G. Web search behavior of internet experts and newbies[J]. Computer Networks,2000,33(1-6):337-346.
[50] Goodrum A A,Bejune M M,Siochi A C. A state transition analysis of image search patterns on the web[C]//Bakker E M,Lew M S,Huang T S,et al. Image and Video Retrieval,July 20-22,2005,Singapore.Heidelberg:Springer Berlin Heidelberg,2003:281-290.
[51] Liu H,Ke2el J V. Combined mining of web server logs and web contents for classifying user navigation patterns and predicting users’future requests[J]. Data&Knowledge Engineering,2007,61(2):304-330.
[52] Hassan A,Jones R,Klinkner K L. Beyond DCG:User behavior as a predictor of a successful search[C]//Proceedings of the Third ACM International Conference on Web Searchand Data Mining,February 3-6,2010,New York City,NY,USA. New York,NY,USA:ACM,2010:221-230.
[53] Shah C,Hendahewa C,Gonzlez-Ib1ez R. Rain or shine? Forecasting search process performance in exploratory search tasks[J]. Journal of the Association for Information Science and Technology,2016,67(7):1607-1623.
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