Patent Publication Number: US-8527442-B2

Title: Method for predicting citation counts

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation in part of the application Ser. No. 12/195,062 filed Aug. 20, 2008 now U.S. Pat. No. 8,275,772 the entire disclosure of which is incorporated herein by reference. Application Ser. No. 12/195,062 is a continuation in part of the application Ser. No. 11/129,388 filed May 16, 2005 now U.S. Pat. No. 7,529,737, the entire disclosure of which is incorporated herein by reference. Application Ser. No. 11/129,138 claims the benefit of U.S. Provisional Appl. No. 60/570,879 filed May 14, 2004, the entire disclosure of which is incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     The most popular method for evaluating the impact and quality of an article is the citation count, which is the number of citations received by an article within a pre-specified time horizon. One limitation of citation count is its unavailability before this horizon expires (typically several years after publication). This delay renders citation counts primarily useful for historical assessment of the scientific contribution and impact of papers. Automatic prediction of citation counts could provide a powerful new method for evaluating articles. Faster identification of promising articles could accelerate research and dissemination of new knowledge. 
     Accurate models for citation count prediction could also improve our understanding of the factors that influence citations. Predicting and understanding article citation counts is however a challenging problem both on theoretical grounds and on the basis of several decades of related empirical work. In fact, the bulk of the literature concerning citation counts addresses the motivating factors for article citations rather than predicting them. 
     Difficulties in making accurate predictions are the sparseness of a citation network and that citation rates may have a degree of randomness. For example, a high impact journal paper may increase the citation rate of papers within the same issue. Previous empirical research predicted long-term citation counts from citations accumulated shortly after publication. For example, linear regression and citation count after 6 months have been used to predict citation counts after 30 months. In doing the analysis for the linear regression, author related information (i.e., the number of previous citations, publications, and co-authors for an author) was incorporated to improve predictions. Further, work has been done to use a regression model for predicting citation counts two years after publication using information available within three weeks of publication. The regression model used seventeen article-specific features and three journal specific features. 
     What is needed is a method and a computer system for predicting citation counts that is more reliable and predicts citation counts for long periods while only using information available at the time of publication of the article and that changes the article and publication technologies based upon the results computed by the system. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention meets the afore-described deficiencies. In one embodiment the present invention includes a computer system and a computerized process to predict citation counts of articles. The process comprises the steps of obtaining, through an input for receiving, an article and a selected set of articles exclusive of the article, storing in a memory the set of articles and the article and extracting through a processor an article feature from each article in the set of articles. The process also includes constructing, through said processor, models using a pattern recognition process and the article feature variable and selecting, through said processor, a best model. A predicting step occurs by the processor to apply the best model to the article to predict a future citation count of the article and the processor outputs the article comprising the future citation count. The output also can change the article&#39;s publication or distribution based on the future citation count. 
     In another embodiment, a computer system programmed to carry out a process to predict a future citation count of an article comprises an input device for receiving an article, a selected set of articles exclusive of the selected article, an author feature for each article of the set of articles and a bibliometric feature for each article in the set of articles. The computer system includes a memory for storing the article, the set of articles, the bibliometric feature, and the author feature and a processor that extracts an article feature from each article in said set of articles and constructs models using a pattern recognition process, the article feature, the author feature and the bibliometric feature. The processor is further configured to select a best model, and predict, using the best model, the future citation count for the article. The computer system further comprises an output device for outputting the article comprising the future citation count to a publication controller which in turn discriminates and publishes the article based upon the outputted computer results. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a process to develop a model to predict the citation count of articles according to one embodiment; 
         FIG. 2  illustrates a process to predict the citation count of articles according to one embodiment; 
         FIG. 3  illustrates how the splitting procedure operates according to one embodiment; 
         FIG. 4  illustrates a process used to optimize parameters of a pattern learning process according to one embodiment; 
         FIG. 5  illustrates a process to determine the influence of each feature used to predict the citation count of an article according to one embodiment; and 
         FIG. 6  illustrates the computer system used in the process in accordance with this invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following detailed description, reference is made to the accompanying drawings, which form a part hereof and show by way of illustration specific embodiments of the present invention. These embodiments are described in sufficient detail to enable those skilled in the art to practice them, and it is to be understood that other embodiments may be utilized, and that logical, and processing changes can be made. 
       FIG. 1  shows a process  100  implemented in a computer system (described in more detail in  FIG. 6 ) for automatically generating a model that allows for predictions of future citation counts of articles. The process  100  includes the steps of: selecting input features  110 , selecting a citation threshold  116 , constructing a corpus  120 , formatting articles for learning  126 , splitting the corpus  30 , training a pattern recognition process  140 , and building a model  150 . 
     The first step in the process  100  is to select input features  110 . The features relate to information about the contents of the article. For example, the selected features are the content of the article&#39;s title or the article&#39;s abstract. In addition, the features can include the content of the body of the article. Further, features include terms associated with the article as provided by article databases such as MESH terms for the MEDLINE database. The features in the MESH terms in the MEDLINE database include the category of the article, terms of the article and whether or not there was federal funding for the project on which the article was written. Any one or combination of the above features can be selected, or none of the above features selected as inputs to create the model. 
     Additional features concerning the article to be selected by the computer processor in step  110 . Information concerning the author of the article can be used by the processor in generating the model. While not all information about an author is relevant such as age or place of birth, information concerning the author&#39;s previous articles and current employment is focused as variables. For example, a variable is the number of articles written by the author. The number of written articles can be the total of all the author&#39;s articles or just the articles written in a certain time frame, such as during the ten years prior to publication of the article. In addition, a variable can be the number of citations that the author received for previous articles. The citations received is the total citations of all the author&#39;s articles or just the citations received in a certain time-frame such as the last ten years. Further, if the author is an academic, the quality of the author&#39;s institution according to the institution&#39;s ranking can be used as a feature. The ranking used in this embodiment was the Academic Ranking of World Universities although any other known ranking methodology can be used. If the article has more than one author, information about every author can be used as a selected input feature. However, information about every author does not have to be used. For example, information about only the first and last author can be used. In addition, certain classes of information about multiple authors can be used, while other classes of information use only information about a single author when information on multiple authors is not available. 
     Other features or variables used include bibliometric features. Bibliometric features can refer to any feature of an article not related to content or the authors. For example, a bibliometric feature is the type of publication in which the article was published. This publication type refers to whether an article is an article or review papers. For example, publication type could be letters to the editor or editorials. Type is identified from the record in the database, such as the Web of Science of the Institute of Scientific Information. Further, bibliometric features can include the impact factor of the journal in which the article was published. The impact factor of a journal, as known in the art, is a commercial product that rates journals based on received citations. Moreover, bibliometric data includes the number of authors that contributed to the article as well as the number of different institutions at which the authors work. Any one of the above bibliometric features or any combination of the above features or none of the above features can be selected as inputs during step  110 . 
     In the present embodiment of the invention, article features, author&#39;s features, and bibliometric features are selected and used in the citation prediction process by the computer process of the system (illustrated in  FIG. 6 ). Any one or combination of the above discussed features in any one of the three discussed categories may be used as the selected features for input in step  110 . For example, the only features selected could be the bibliometric journal impact factor feature. The use of more features in most cases leads to a better ability to correctly predict the citation count of an article. In addition, an advantage of the described features is that all the features are available at the time that the article publishes. The process is not based on citations received by an article or other information that cannot be readily obtained at or before publication of the article. 
     The process  100  as described in  FIG. 1  determines whether an article will receive a certain threshold number of citations. In step  116 , the citation threshold is selected. Any number of citations may be selected for the threshold. For example, if a threshold of 100 citations is chosen, then the process will predict if an article will receive 100 or more citations or less than 100 citations. The process  100  does not predict an exact number of citations for an article, but answers a question of whether the article will receive as much as or more citations then the citation threshold as selected in step  116 . The threshold number can be used as basis by, for example, the publisher to determine whether or not an article merits publication, distribution or review. 
     At step  120 , the corpus is constructed by the system. The corpus is a set of articles and article information that are used by the pattern recognition process to build a model. The corpus can be composed of any articles. For the process  100  to more accurately predict citation counts, the articles should relate to the same general field as the article for which the citation count is to be predicted. However, the corpus articles do not have to belong to the same field as the article for which the citation count is to be predicted. In addition, for an accurate prediction of future citations at least a single article must have a citation count above the citation threshold selected in  116 . The better the distribution of articles with citation counts above and below the citation threshold the more likely that the predicted future citations will be more accurate. Once the articles are selected, each article is given a positive or negative label. If the article&#39;s citation count exceeds the threshold it is given a positive label. If the citation count does not exceed the threshold the article is given a negative label. 
     Further, for the process  100  to produce accurate predictions the corpus should be sufficiently large. A small corpus would still allow the process  100  to predict a citation count; however the prediction will likely not be as accurate as a prediction developed using a process  100  having a larger number of articles in the corpus. 
     In step  126 , the articles are formatted by the processor so that the pattern recognition process may use the articles and relevant article information as inputs. In one embodiment, the title, abstract and database words from MEDLINE are features that are extracted and then formatted. The features selected in step  110  of the articles are extracted. The features from the articles are then formatted by removal of stop words. Stop words are words such as “the,” “a,” “other,” etc. that do not assist the pattern recognition process. Once all stop words are removed, the remaining words are stemmed. Stemming reduces words to their roots. For example the terms “randomly,” “randomness,” and “randomize” all describe a similar state yet each word would be recognized by a pattern recognition process as a different word. When the words are stemmed they are all reduced to the word “random.” Thus, stemming increases the effective sample by encoding the term “random” three times rather than encoding the other three terms once. The Porter stemming algorithm is used to stem words, although other known stemming algorithms could be used. 
     The article features are further formatted to be numerical values to be used by the pattern recognition process. To format the features into numerical values, a log frequency with redundancy algorithm is used. The log frequency with redundancy algorithm weights words based on their usefulness to the pattern recognition process. Words that appear frequently in many articles are assumed to be less helpful than more selective words that appear in fewer articles. This weighting algorithm was chosen due to its superior performance when used with pattern recognition processes. Alternatively other weighting schemes known in the art can be used. 
     The rest of the features from the corpus articles must also be formatted to be used by the pattern recognition process. The bibliometric and author features are given a value from zero to one. The value given is a normalization of the value of the feature as compared to similar features from other articles in the corpus. To normalize the value of the feature, the lowest and highest value of the feature is determined in the corpus. The lowest value is normalized as zero and the highest as a one. The rest of the values are assigned corresponding values. For example, in the entire corpus if the lowest citation count for previous work by an author of an article was fifty and the highest was five hundred and fifty, then the article with a count of fifty would receive a zero for that feature and the article with the highest count would receive a one. Following the above example, if an article had a count that was two hundred and fifty, it would be normalized to one-half (0.5) and given that formatted value. 
     Once all the features of the articles have been formatted the features may be reduced. This reduction can be done by any number of reducing algorithms. These algorithms include, Markov Blanket, regularized learning models, univariate feature selection and wrapping methods. 
     In step  130 , the corpus is split by the processor. The splitting of the corpus process is described in  FIG. 3 .  FIG. 3  shows a corpus  310 , a training set  320 , a validation set  322 , an individual set  332  and sets  330 . During step  130  the labeled corpus articles  310  are split into n-fold sets  330 , where each individual set  332  is split into categories. Each individual set  332  contains all the corpus articles  310 . The number n of the n-fold sets  330  is chosen based on the frequency of positive or negative labeled articles as determined during corpus construction in step  120 . The choice for n should allow for sufficient articles from each category in each individual set  332 . However, the number of sets used may range from 1 to the number of articles in the corpus  310 . One embodiment of the procedure illustrated in  FIG. 3  is one in which the corpus  310  is split into ten individual sets  332 . The individual sets  332  are distinguished from one another according to which articles of the corpus  310  are placed in certain categories as illustrated in  FIG. 3 . The splitting procedure in step  130  is done to ensure that the filtering models selected are not a by-product of a particularly favorable or bad split of the articles. 
     The corpus articles  310  in an individual set  332  are further divided into two categories. The two categories are the training category  320  and the validation category  322 . The union of the training category  320  and validation category  322  is the complete corpus  310  which form an individual set  332 . Each category should contain approximately the same proportion of negative and positive articles as the entire corpus  310 . The training category articles  320  are used to build filtering models, the validation category articles  322  are used to optimize specific parameters for the pattern recognition process that build the filtering models 
     The articles from the validation category  322  from each set  332  are mutually exclusive of the articles of the validation category  322  in the nine remaining individual sets  332 . As such, the union of the articles from the validation category  322  from each set  332  is all the corpus articles  310 . For example if one-hundred corpus articles were made into ten sets, then each set  332  would have ten articles in their training category. One set  332  of the ten sets  330  would have articles  1 - 10  in its validation category  322 , another set  332  of the ten sets  332  would have articles  11 - 20  in its validation category  322 . The remaining articles would be divided into the remaining eight sets  332  as described above. Once articles are assigned to a validation category  322  in an individual set  332 , the remaining articles are sent to the training category  320 . 
     In step  140 , the pattern recognition process is run by a processor. A pattern recognition process takes the formatted features of each article, and based on those features learns to distinguish between positively and negatively labeled articles. In essence the pattern recognition process learns what features an article needs to accumulate a citation count above the threshold and what features or lack of features would cause the article to not receive enough citations to reach the citation threshold. The pattern recognition process used by a processor in the exemplary embodiment is the Support Vector Machine (SVM) classification algorithm. The SVM calculates maximal margin hyperplane(s) separating two or more classes of data. The basic algorithm employed by the computer is reproduced below where K represents a kernel and C a penalty parameter: 
     
       
         
           
             
               
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     The SVM algorithm used in the computer was implemented in suitable computer code with a polynomial kernel. Other common kernels include but are not limited to RBF, two layer neural network kernels and other applicable kernels can be used in the SVM algorithm by the processor. The polynomial kernel used is reproduced below:
 
 K ( x   i   ,x   j )=( x   i   xg   i +1) d  
 
     Two parameters in the SVM algorithm need to be optimized for each corpus  310  that is being used to develop a filtering model. The parameters are the penalty parameter C and the degree d of the polynomial kernel. Before the parameters can be optimized, a finite range of parameters must be selected. In an exemplary embodiment, parameter C is construed over the range {0.1, 0.2, 0.4, 0.7, 0.9, 1, 5, 10, 20}. The degree d of the polynomial kernel is limited to the range {1, 2, 3, 4, 5, 8}. The ranges were selected based on previous research. Larger ranges can be used; however the larger range will increase the time required by the processor to generate a filtering model. The selection of parameters from a range allows for the SVM algorithm to be fine-tuned to allow for the best model to be developed based upon a corpus made in step  120 . Because of the range of possible parameters, various combinations of the parameters C and d exist. Thus, each individual combination of the parameters is used to develop a model and then validated to determine the optimal combination for that corpus made in step  120 . 
     Referring now to  FIG. 4 , the process  410  is shown for selecting an optimal combination of parameters. A single combination of parameters C and d are selected in step  420 . The SVM algorithm is then implemented with the combination of parameters. In step  430 , the processor executes the algorithm and generates a filtering model using the training category  420  articles from a set  332 . In step  440 , the newly generated model from step  430  is validated by the processor using the validation category  322  articles from the same set  332  used in step  430 . The performance of the model generated from the processed combination of parameters is recorded and stored in the memory (shown in  FIG. 6 ). The performance is based on how many of the articles from the validation category  332  the model labels correctly. During step  450 , steps  430  and  440  are then repeated on each of the individual sets  332  created during step  130 . From the exemplary embodiment with ten different individual sets  332 , steps  430  and  440  are repeated ten times. The recorded performance from each set  332  of the total sets  330  are averaged or combined for a composite performance measure for that combination of parameters. In step  460 , the process of determining the composite performance measure for a combination of parameters is then repeated by the processor for each individual combination of parameters C and d. The combination of parameters with the highest composite performance is noted, and stored in memory. 
     In step  150 , the optimal combination of parameters found in step  140  is used to build a final learning model. All of the articles are used by the pattern recognition process that is implemented with the optimized parameters found in step  140  to make a final model that will be used to accurately predict the future citation counts of articles. 
     Using the process described above when making a model helps to ensure that the model is sufficiently general to perform well on data that the model has not seen before. The output of the model produced is non-probabilistic. However, the output of a model developed according to the method described above can be converted to probabilities using standard probability fitting methods. Clustering methods can also further organize the results from the filtering models into coherent subgroups of articles; automatic identification of prominent keywords can characterize the contents of each automatically-created cluster. The above implementation could also be accomplished via bootstrapping, leave-one-out, or holdout methods. 
       FIG. 2  is a process for using a model to determine a future citation count of an article of interest. The process  200  includes the steps of: obtaining the article of interest  210 , formatting the article  220 , selecting a citation threshold  230 , applying a model with that threshold  240  and outputting the prediction  250 . 
     In step  210 , the article and article information of interest is obtained. In step  220 , the features related to the article are formatted by a processor or input unit in the same manner that features were formatted in step  126 . In step  230 , the citation threshold is chosen by a user or by a device and inputted into the system for the article. The citation threshold is significant since the process  200  predicts whether or not the article will receive citations that equal or exceed the citation threshold or fail to receive the threshold number of citations. In step  240 , a model is built by the processor or chosen from memory with the same citation threshold selected in step  230  and applied to the features of the article. In step  250 , the model is then executed by the processor and the processor outputs a determination whether the article selected in step  210  will reach or exceed the threshold citation level selected in step  230  or fail to meet that citation threshold. The output is then, for example, applied to a connected external device such as a printer, or a server which published or distributes the article based upon it exceeding such threshold. 
     Using the process described in  FIG. 2  to predict the number of future citations yields numerous advantages. The process described uses features that are available at or before the time of publication. Thus, the future citation count can be predicted before the article is published and does not rely on features collected after publication such as a short term citation count. Further, the process can predict citation counts for numerous time periods, depending on how the corpus is constructed. Citation counts over a longer period of time better indicate the importance of an article than citation counts accumulated over shorter time periods. In addition, using the process above the performance of the predictions as measured by area under the receiver operating characteristic curve (AUC) ranged from 0.86 to 0.92 depending on the threshold chosen. This is significant since an AUC of 0.85 indicates a very good prediction and an AUC above 0.9 indicates an excellent prediction. 
       FIG. 5  describes a process to determine which of the features used in process  200  described in  FIG. 2  were the most influential. The process includes the steps of: using a processor to build a model with a learning process  510 , running feature selection  520  through the processor, outputting influential features  530 , fitting a statistical model on the influential features  540  and outputting the results  550  for application described in conjunction with  FIG. 6 . 
     In step  510 , a process described in  FIG. 1  to construct a model using features of selected articles is accomplished. During this step, a threshold citation count is determined by the processor. In step  520 , a feature selection process, such as the Markov Blanket algorithm, is employed by the processor to reduce the features to those that were the most influential in making the predicted citation count. Other feature selection processes may be used by the computer such as regularized learning models, univariate feature selection and wrapping methods. In step  530 , the influential features are outputted by the processor and stored. In step  540 , the stored influential features are applied by the processor to a statistical model such as a logistical regression model. Other statistical models may be used. The statistical model then computes how much more likely having certain features in an article will lead to the article receiving a citation count above the threshold set in step  510 . In step  550 , the results of the statistical model are then outputted as previously described in  FIG. 2 . 
     As noted, exemplary processes for creating the filtering models are implemented as a collection of software programs that can be run automatically on computer hardware and on article production equipment, such as industry standard printers/printing processes.  FIG. 6  shows hardware for generating the filtering models comprising computers  610 ,  620 ,  630 , and  640 , a processor  620 , a memory  614 , an input/output device  616 , a network connection  650 , a publication controller  660 , first, second and third links  670 ,  672 , and  674 , a database  680 , a printer or printing press  685  and a display  690 . 
     In an embodiment, a single computer  610  is an example of hardware to generate the models. The computer  610  comprises a processor  612  and memory  614 . The memory  614  must be large enough to handle the computations necessary to generate the models. The input/output  616  receives information and sends information. 
     In another embodiment the models are generated with a cluster of computers comprising computers  610 ,  620 ,  630 , and  640 . Each computer has a processor  612  and memory  614 . In an exemplary embodiment each computer  610 ,  620 ,  630 , and  640  has 4 GB of RAM memory  614  and a Pentium 4 processor  612 . The computer cluster is connected together by a network connection  650  and each computer  610 ,  620 ,  630 , and  640  is running Linux. 
     In another embodiment, the process is contained in the memory  614  and runs on the processor  612 . In another embodiment, the process described above is on a computer readable medium and runs on the processor  612 . In another embodiment, the process runs on any one of the single computers  610 ,  620 ,  630 ,  640  or combination thereof. The models can also be generated on a field programmable gate array or other various hardware systems. 
     In another embodiment, the process is contained in the memory  614  and runs on the processor  612  in the computer  610 . The processor  612  takes an input of an article and information concerning the article and stores the article in the memory  614 . The processor then performs the process as described in an embodiment to produce an article with a predicted citation count. The computer then outputs the article with the predicted citation count by way of the input/output  616 . The article with the predicted citation count is a more practical tool for evaluating the quality and impact of the recent article, its authors and subject because no wait is required to determine citation counts. To evaluate an article without the predicted citation requires time for citations to be accumulated to be used as a basis for evaluation. Thus, an article with a predicted citation count can more easily be evaluated and is more likely to be published since the predicted success of the article is known. 
     In another embodiment, the process is contained in the memory  614  and runs on the processor  612  in the computer  610 . The processor  612  takes an input of an article and information concerning the article and stores the article in the memory  614 . The processor then performs the process as described in an embodiment to produce an article with a predicted citation count. The article with the predicted citation count is sent to computer  620  through the network connection  650 . Computer  620  determines if the article with the predicted citation count should be published based partially on the predicted citation count and other factors. If the article with the predicted citation count is to be published, the computer  620  sends the article to be published. If the article is not to be published the computer  620  sends a rejection of publication. Thus, an article with a sufficient predicted citation count is published. 
     Various devices are controlled through publication controller  660  that use the output of computer  610  or computers  610 ,  620 ,  630  and  640 . The controller  660  is connected to the computers  610 ,  620 ,  630  and  640  through network connection  650 . Further the controller  660  is connected to a database  680  through a first link  670 , a printer or printing press  685  through a second link  672 , and a display  690  through a third link  674 . The controller  660  takes the output, the stored article with a predicted citation count above the threshold, from the computer  610  or computers  610 ,  620 ,  630  and  640 . The article is then, in turn, sent by the controller  660  to the printer  685  through link  672  to be printed as a hard copy. Alternatively, the approved article can be sent via the first link  670  to another device, such as a publication database  685 . The database  685  can, in turn, be connected to standard publishing equipment, such as a printing press. Alternatively, the approved article, threshold, model or other processed information, referenced above, can be sent by controller  660 , through the third link  674  to display  690  for display to an end user. 
     The above description and drawings illustrate preferred embodiments which achieve the objects, features, and advantages of the present invention. Although certain advantages and preferred embodiments have been described above, those skilled in the art will recognize that substitutions, additions, deletions, modifications and/or other changes may be made without departing from the spirit or scope of the invention. Accordingly, the invention is not limited by the foregoing description but is only limited by the scope of the appended claims.