The Asia-Pacific Software Engineering Conference (APSEC) is a reputed and a long-running conference which has successfully completed more than two decades as of year 2015. We conduct a bibliometric and scientific publication mining based study to how the conference has evolved over the recent past six years (year 2010 to 2015). Our objective is to perform in-depth examination of the state of APSEC so that the APSEC community can identify strengths, areas of improvements and future directions for the conference. Our empirical analysis is based on various perspectives such as: paper submission acceptance rate trends, conference location, scholarly productivity and contributions from various countries, analysis of keynotes, workshops, conference organizers and sponsors, tutorials, identification of prolific authors, computation of citation impact of papers and contributing authors, internal and external collaboration, university and industry participation and collaboration, measurement of gender imbalance, topical analysis, yearly author churn and program committee characteristics.
In this Section, we present related work and the novel contributions of the study presented in this paper in context to existing work. analyze research papers published in MSR (Mining Software Repositories) series of conferences from @math to @math (a period of @math years) @cite_0 . conduct a bibliometric study consisting of mining @math papers in Requirements Engineering (RE) series of conference of @cite_6 . analyze @math years of RE papers published from the year @math to @math . They study several aspects such as: authorship numbers and scholarly productivity of various countries or regions, interdisciplinarity, topic modeling and categorization, collaboration (university and industry, internal and external) and public and proprietary dataset @cite_6 . conduct a research study on gender imbalance and low participation of women in Computer Science Research (CSR) @cite_11 . They conduct several empirical and statistical analysis consisting of mining thousands of bibliometric entries in DBLP http: dblp.uni-trier.de bibliography data @cite_11 . Their findings reveal that in the broad field of Computer Science, there is a gender balance wherein only @math 740 @math 2001 @math 2010 @math 2010 @math 2015$) through arXiv open access https: arxiv.org .
@cite_0 @cite_6 @cite_11
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The low participation by women authors in research is an important equity issue in Computer Science Research (CSR). There are various parameters and methodologies that can be used to measure the gender imbalance. In this paper, we present a study on gender gap, imbalance and women participation in CSR. We conduct our experiments on DBLP bibliographical database and analyze several years of publication dataset across various domains of CSR. We perform Exploratory Data Analysis on the bibliographical dataset and study the trend of gender imbalance over several years. We propose eight research questions across various facets and our results shows a significant gender imbalance in different sub-fields within CSR and low rate of women participation across various regions of world. We present insights from a bibliometric analysis and scientific paper publication mining of 551 papers in Requirements Engineering (RE) series of conference (11 years from 2005 to 2015). We study cross-disciplinary and interdisciplinary nature of RE re- search by analyzing the cited disciplines in the reference section of each paper. We apply topic modeling on a corpus consisting of 551 abstracts and extract topics as frequently co-occurring and connected terms. We use topic modeling to study the structure and composition of RE research and analyze popular topics in industry as well as research track. Co-authorship in papers is an indicator of collaboration and interaction between scientists as well as institutions and we analyze co-authorship data to in- vestigate university-industry collaboration, internal and external collaborations. We present results on the distribution of the num- ber of co-authors in each paper as well as distribution of authors across world regions. We present our analysis on the public or proprietary dataset as well as the domain of the dataset used in studies published in Requirements Engineering (RE) series of conferences. T his document is the report of a workshop that convened a group of experts to discuss the recruitment and retention of women in Computer Science and Engineering (CSE) Graduate Programs. (1) Participants included long-time members of the CSE academic and research communities, social scientists engaged in relevant research, and directors of successful retention efforts. (2) The report is a compendium of the experience and expertise of workshop participants, rather than the result of a full-scale, scholarly study into the range of issues. Its goal is to provide departments with practical advice on recruitment and retention in the form of a set of specific recommendations. Women are significantly underrepresented in CSE academic departments [5][40]. As computing technology becomes increasingly pervasive, this underrepresentation translates into a loss of opportunity for individuals, a loss of talent to the workforce, and a loss of creativity in shaping the future of society. While there are many causes of this underrepresentation some rooted in early socialization and primary educational experiences academic departments at the university level nevertheless can have an effect [6][7]. In particular, an improvement at the graduate level in recruitment and retention (and thus in graduation rates) would enable more women to move into visible and influential positions in the CSE community. The increasing presence of these women would provide positive role models and mentors. In order to treat all students fairly, educators must pay attention to gender-based traits. Although in characterizing behaviors one must be careful to acknowledge the existence of individual differences and to avoid stereotyping, there is a large body of information on gender traits. There is strong evidence, for example, that women, even though they perform at the same levels, have less confidence in their abilities and individual accomplishments than men [2][17][36][39][43]. Women are often less aggressive than male students in promoting themselves, attempting new or challenging activities, and pursuing awards or fellowships. There is evidence that females come to computing as only one interest among many, and are thus less single-minded than their male counterparts [27]. Often women report feeling "out of place" in the male-dominated, hacker culture [3] [22] [28]. In light of such differences, some of our recommendations are gender-specific. Most, however, are not. The adoption of our recommendations would improve the educational environment for all students. The recommendations are given in two sections, the first Let f be a random Boolean formula that is an instance of 3-SAT. We consider the problem of computing the least real number k such that if the ratio of the number of clauses over the number of variables of f strictly exceeds k , then f is almost certainly unsatisfiable. By a well-known and more or less straightforward argument, it can be shown that kF5.191. This upper bound was improved by to 4.758 by first providing new improved bounds for the occupancy problem. There is strong experimental evidence that the value of k is around 4.2. In this work, we define, in terms of the random formula f, a decreasing sequence of random variables such that, if the expected value of any one of them converges to zero, then f is almost certainly unsatisfiable. By letting the expected value of the first term of the sequence converge to zero, we obtain, by simple and elementary computations, an upper bound for k equal to 4.667. From the expected value of the second term of the sequence, we get the value 4.601q . In general, by letting the U This work was performed while the first author was visiting the School of Computer Science, Carleton Ž University, and was partially supported by NSERC Natural Sciences and Engineering Research Council . of Canada , and by a grant from the University of Patras for sabbatical leaves. The second and third Ž authors were supported in part by grants from NSERC Natural Sciences and Engineering Research . Council of Canada . During the last stages of this research, the first and last authors were also partially Ž . supported by EU ESPRIT Long-Term Research Project ALCOM-IT Project No. 20244 . †An extended abstract of this paper was published in the Proceedings of the Fourth Annual European Ž Symposium on Algorithms, ESA’96, September 25]27, 1996, Barcelona, Spain Springer-Verlag, LNCS, . pp. 27]38 . That extended abstract was coauthored by the first three authors of the present paper. Correspondence to: L. M. Kirousis Q 1998 John Wiley & Sons, Inc. CCC 1042-9832r98r030253-17 253 Mining Software Repositories (MSR) is an applied and practise-oriented field aimed at solving real problems encountered by practitioners and bringing value to Industry. We believe that empirical studies on both Open Source Software (OSS) and Closed or Proprietary Source (CSS PSS) is required in MSR research to increase generalizability or transferability of findings and reduce external (or threats) validity concerns. Furthermore, we believe that a collaboration between University and Industry is must or important in achieving the stated goals and agenda of MSR research (such as deployment and technology transfer). We analyse past five years of research papers published in MSR series of conferences (2010-2014) and count the number of studies using solely OSS data or solely CSS data or both OSS and CSS data. We also count the number of papers published by authors solely from Universities, solely from Industry and from both University and Industry. We present our findings which indicate lack of University-Industry collaboration (measured using co-authorship in scientific publications) and paucity of empirical studies on CSS PSS data. Our analysis reveals that out of 187 studies over a period of 5 years, 90:9 studies are conducted solely on OSS dataset. We present our findings which indicate that only 14:43 of the studies involve a University-Industry collaboration. Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science. About the Editors:Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has worked as a researcher at IBM since then, and has published over 130 papers in major data mining conferences and journals. He has applied for or been granted over 70 US and International patents, and has thrice been designated a Master Inventor at IBM. He has received an IBM Corporate award for his work on data stream analytics, and an IBM Outstanding Innovation Award for his work on privacy technology. He has served on the executive committees of most major data mining conferences. He has served as an associate editor of the IEEE TKDE, as an associate editor of the ACM SIGKDD Explorations, and as an action editor of the DMKD Journal. He is a fellow of the IEEE, and a life-member of the ACM. Haixun Wang is currently a researcher at Microsoft Research Asia. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He subsequently worked as a researcher at IBMuntil 2009. His main research interest is database language and systems, data mining, and information retrieval. He has published more than 100 research papers in referred international journals and conference proceedings. He serves as an associate editor of the IEEE TKDE, and has served as a reviewer and program committee member of leading database conferences and journals. An analysis of three major problems in the application of bibliometric research performance indicators is made in three separate sections. In the first section, the influence of field-dependent citation practices is analysed. The results indicate that rankings of publications from different fields, based on citation counts, can be affected seriously by differences between citation characteristics in those fields. If certain assumptions hold, one should expect high (short term) citation levels in Biochemistry, Celbiology and Biophysics. Medium citation levels are to be expected in Experimental and Molecular Physics, Physical and Organic Chemistry, Pharmacology and Plant Physiology, and low citation levels in Mathematics, Taxonomy, Pharmacognosy and Inorganic Solid State Chemistry. In the second section time-dependent factors are studied. It is shown that trend-analyses of output and impact based on bibliometric scores can be disturbed by changes in theSCI-database and in publication and citation practices. One of the disturbing factors is shown to be the inclusion of so called Books into theSCI data-base in 1977. Finally, in the third section a case is presented which illustrates the consequences of operating on incomplete bibliometric data in the evaluation of scientific performance. A completeness percentage of 99 for publication data is proposed as a standard in evaluations of the performance of small university research groups). Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention. We complement several recent papers in this line of research by introducing a general method to reduce bias in the data through random projections in a fair'' subspace. We apply this method to densest subgraph and @math -means. For densest subgraph, our approach based on fair projections allows to recover both theoretically and empirically an almost optimal, fair, dense subgraph hidden in the input data. We also show that, under the small set expansion hypothesis, approximating this problem beyond a factor of @math is NP-hard and we show a polynomial time algorithm with a matching approximation bound. We further apply our method to @math -means. In a previous paper, [NIPS 2017] showed that problems such as @math -means can be approximated up to a constant factor while ensuring that none of two protected class (e.g., gender, ethnicity) is disparately impacted. We show that fair projections generalize the concept of fairlet introduced by to any number of protected attributes and improve empirically the quality of the resulting clustering. We also present the first constant-factor approximation for an arbitrary number of protected attributes thus settling an open problem recently addressed in several works. While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task. In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications. This paper presents, to our knowledge, the first study on analyzing mathematical expressions to detect academic plagiarism. We make the following contributions. First, we investigate confirmed cases of plagiarism to categorize the similarities of mathematical content commonly found in plagiarized publications. From this investigation, we derive possible feature selection and feature comparison strategies for developing math-based detection approaches and a ground truth for our experiments. Second, we create a test collection by embedding confirmed cases of plagiarism into the NTCIR-11 MathIR Task dataset, which contains approx. 60 million mathematical expressions in 105,120 documents from arXiv.org. Third, we develop a first math-based detection approach by implementing and evaluating different feature comparison approaches using an open source parallel data processing pipeline built using the Apache Flink framework. The best performing approach identifies all but two of our real-world test cases at the top rank and achieves a mean reciprocal rank of 0.86. The results show that mathematical expressions are promising text-independent features to identify academic plagiarism in large collections. To facilitate future research on math-based plagiarism detection, we make our source code and data available. In sparse principal component analysis we are given noisy observations of a low-rank matrix of dimension @math and seek to reconstruct it under additional sparsity assumptions. In particular, we assume here each of the principal components @math has at most @math non-zero entries. We are particularly interested in the high dimensional regime wherein @math is comparable to, or even much larger than @math . In an influential paper, johnstone2004sparse introduced a simple algorithm that estimates the support of the principal vectors @math by the largest entries in the diagonal of the empirical covariance. This method can be shown to identify the correct support with high probability if @math , and to fail with high probability if @math for two constants @math . Despite a considerable amount of work over the last ten years, no practical algorithm exists with provably better support recovery guarantees. Here we analyze a covariance thresholding algorithm that was recently proposed by KrauthgamerSPCA . On the basis of numerical simulations (for the rank-one case), these authors conjectured that covariance thresholding correctly recover the support with high probability for @math (assuming @math of the same order as @math ). We prove this conjecture, and in fact establish a more general guarantee including higher-rank as well as @math much smaller than @math . Recent lower bounds berthet2013computational, ma2015sum suggest that no polynomial time algorithm can do significantly better. The key technical component of our analysis develops new bounds on the norm of kernel random matrices, in regimes that were not considered before. In this paper we settle several longstanding open problems in theory of indexability and external orthogonal range searching. In the rst part of the paper, we apply the theory of indexability to the problem of two-dimensional range searching. We show that the special case of 3-sided querying can be solved with constant redundancy and access overhead. From this, we derive indexing schemes for general 4-sided range queries that exhibit an optimal tradeo between redundancy and access overhead. In the second part of the paper, we develop dynamic external memory data structures for the two query types. Our structure for 3-sided queries occupies O(N=B) disk blocks, and it supports insertions and deletions in O(log B N) I Os and queries in O(log B N + T=B) I Os, where B is the disk block size, N is the number of points, and T is the query output size. These bounds are optimal. Our structure for general (4-sided) range searching occupies O (N=B)(log(N=B))= log log B N disk blocks and answers queries in O(log B N + T=B) I Os, which are optimal. It also supports updates in O (log B N)(log(N=B))= log log B N I Os. Center for Geometric Computing, Department of Computer Science, Duke University, Box 90129, Durham, NC 27708 0129. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04 96 1 0013 and by the National Science Foundation through ESS grant EIA 9870734. Part of this work was done while visiting BRICS, Department of Computer Science, University of Aarhus, Denmark. Email: large@cs.duke.edu. yDepartment of Computer Sciences, University of Texas at Austin, Austin, TX 78712-1188. Email vsam@cs.utexas.edu zCenter for Geometric Computing, Department of Computer Science, Duke University, Box 90129, Durham, NC 27708 0129. Supported in part by the U.S. Army Research O ce through MURI grant DAAH04 96 1 0013 and by the National Science Foundation through grants CCR 9522047 and EIA 9870734. Part of this work was done while visiting BRICS, Department of Computer Science, University of Aarhus, Denmark and I.N.R.I.A., Sophia Antipolis, France. Email: jsv@cs.duke.edu. We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer. We explore if and how Microsoft Academic (MA) could be used for bibliometric analyses. First, we examine the Academic Knowledge API (AK API), an interface to access MA data, and compare it to Google Scholar (GS). Second, we perform a comparative citation analysis of researchers by normalizing data from MA and Scopus. We find that MA offers structured and rich metadata, which facilitates data retrieval, handling and processing. In addition, the AK API allows retrieving frequency distributions of citations. We consider these features to be a major advantage of MA over GS. However, we identify four main limitations regarding the available metadata. First, MA does not provide the document type of a publication. Second, the "fields of study" are dynamic, too specific and field hierarchies are incoherent. Third, some publications are assigned to incorrect years. Fourth, the metadata of some publications did not include all authors. Nevertheless, we show that an average-based indicator (i.e. the journal normalized citation score; JNCS) as well as a distribution-based indicator (i.e. percentile rank classes; PR classes) can be calculated with relative ease using MA. Hence, normalization of citation counts is feasible with MA. The citation analyses in MA and Scopus yield uniform results. The JNCS and the PR classes are similar in both databases, and, as a consequence, the evaluation of the researchers' publication impact is congruent in MA and Scopus. Given the fast development in the last year, we postulate that MA has the potential to be used for full-fledged bibliometric analyses. We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0 98 7... 5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 mitchell+@cs.cmu.edu During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. @PARASPLIT This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for wide'' data (p bigger than n), including multiple testing and false discovery rates. @PARASPLIT Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. This paper recommends methods for increasing female participation in undergraduate computer science. The recommendations are based on recent and on-going research into the gender gap in computer science and related disciplines They are intended to work in tandem with the Computing Research Association's recommendations for graduate programs (see [18] in this issue) to promote a general increase in women's participation in computing professions.Most of the suggestions offered here could improve the educational environment for both male and female students. However, general improvements are likely to be of particular benefit to women because women in our society do not generally receive the same level of support that men receive for entering and persisting in this field. Parents, friends, and classmates seldom encourage women to choose and continue in a computing discipline. Those few women who declare a computing major tend to experience continued lack of support throughout their education. They have few female peers to call on for help. Furthermore, students in their own and other majors may consider them odd. Even faculty can discourage women with unthinking remarks or with expectations based on the assumption that men's behavior and experience are the basis for success in computing majors. This lack of support can block or weaken women's commitment to computing. Moreover, when conditions in a department are generally unfavorable, those with a weakened commitment to the discipline often leave at higher rates than those who have sufficient support to overcome the conditions. When analysing human activities using data mining or machine learning techniques, it can be useful to infer properties such as the gender or age of the people involved. This paper focuses on the sub-problem of gender recognition, which has been studied extensively in the literature, with two main problems remaining unsolved: how to improve the accuracy on real-world face images, and how to generalise the models to perform well on new datasets. We address these problems by collecting five million weakly labelled face images, and performing three different experiments, investigating: the performance difference between convolutional neural networks (CNNs) of differing depths and a support vector machine approach using local binary pattern features on the same training data, the effect of contextual information on classification accuracy, and the ability of convolutional neural networks and large amounts of training data to generalise to cross-database classification. We report record-breaking results on both the Labeled Faces in the Wild (LFW) dataset, achieving an accuracy of 98.90 , and the Images of Groups (GROUPS) dataset, achieving an accuracy of 91.34 for cross-database gender classification. The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Authorship analysis, an important part of text mining, attempts to learn about the author of the text through subtle variations in the writing styles that occur between gender, age and social groups. Such information has a variety of applications including advertising and law enforcement. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to identify the gender of users on Twitter using Perceptron and Nai ve Bayes with selected 1 through 5-gram features from tweet text. Stream applications of these algorithms were employed for gender prediction to handle the speed and volume of tweet traffic. Because informal text, such as tweets, cannot be easily evaluated using traditional dictionary methods, n-gram features were implemented in this study to represent streaming tweets. The large number of 1 through 5-grams requires that only a subset of them be used in gender classification, for this reason informative n-gram features were chosen using multiple selection algorithms. In the best case the Naive Bayes and Perceptron algorithms produced accuracy, balanced accuracy, and F-measure above 99 .