1,720,956 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
ANALISIS BRAND LAYANAN AKADEMIK PERGURUAN TINGGI INDONESIA MENGGUNAKAN KLASIFIKASI TEKS DI MEDIA SOSIAL
Full text link Penelitian ini bertujuan untuk menganalisis persepsi komunitas eksternal terhadap brand akademik perguruan tinggi di Indonesia melalui media sosial, khususnya Twitter/X. Seiring dengan tingginya jumlah perguruan tinggi dan angka partisipasi kasar (APK), kompetisi antar institusi pendidikan tinggi semakin kuat, mendorong perlunya diferensiasi brand yang disampaikan ke publik. Dalam studi ini, dikumpulkan post dari 30 akun resmi X perguruan tinggi di Indonesia yang kemudian diklasifikasikan ke dalam lima kategori brand akademik: Innovative, Global Impact, Student Engagement, Career Focused, dan Research Excellent. Proses klasifikasi dilakukan dengan membangun model pembelajaran menggunakan algoritma Naïve Bayes, yang diimplementasikan melalui pustaka pemrosesan bahasa alami di lingkungan Node.js. Untuk mengevaluasi kinerja model, dilakukan pengujian terhadap dataset uji terpisah, dan dihitung metrik evaluasi berupa precision, recall, dan accuracy berdasarkan nilai True Positive, False Positive, dan False Negative yang diperoleh melalui confusion matrix untuk setiap kelas. Hasil evaluasi menunjukkan bahwa model yang dikembangkan memiliki performa nilai rata-rata precision sebesar 80,8%, recall sebesar 78,8%, dan accuracy sebesar 80%, sehingga dapat diandalkan sebagai alat bantu untuk memahami kesesuaian antara brand yang dikomunikasikan dan persepsi publik secara daring. Kata Kunci— brand akademik, brand perguruan tinggi, klasifikasi teks, naïve bayes, media sosial. ABSTRACTThis study aims to analyze the perceptions of external communities regarding the academic branding of Indonesian universities through social media, particularly Twitter/X. With the growing number of higher education institutions and rising gross enrollment rates, competition among universities has intensified—prompting the need for more distinct and strategic public brand positioning. In this study, posts were collected from 30 official university X accounts in Indonesia and categorized into five academic brand themes: Innovative, Global Impact, Student Engagement, Career Focused, and Research Excellent. The classification process involved building a supervised machine learning model using the Naïve Bayes algorithm, implemented with a natural language processing library in the Node.js environment. To evaluate the model's performance, a separate test dataset was used, and evaluation metrics—namely precision, recall, and accuracy—were calculated for each class based on values of True Positive, False Positive, and False Negative derived from a confusion matrix. The results indicate that the developed model performs well, achieving average scores of 80,8% for precision, 78,8% for recall, and 80% for accuracy, making it a reliable tool for assessing the alignment between institutional brand communication and public perception in online discourse. Keywords—academic brand, university brand, text classification, naïve bayes, social media.
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
ANALISIS SENTIMEN PADA TWEET TERKAIT VAKSIN COVID-19 MENGGUNAKAN METODE SUPPORT VECTOR MACHINE
Full text linkCovid-19 is a disease that has been declared a global pandemic since March 2020. One of the challenges in dealing with the current Covid-19 pandemic is the widespread doubts about the use of vaccines, even though vaccination is one of the most successful ways to deal with infectious disease outbreaks. Vaccine hesitancy can be observed, among others, from public sentiment or perception on social media, one of them is Twitter. The existence of social media can affect the absorption of information received by a person, in this case social media is also a medium for anti-vaccine propaganda which can result in a decrease in public confidence in the Covid-19 vaccine. This study aims to develop a classification model using the Support Vector Machine (SVM) method for sentiment analysis of Tweet related to the Covid-19 vaccine. Several previous studies have conducted sentiment analysis related to Covid-19, but this research specifically conducts sentiment analysis on the topic of the Covid-19 vaccine so that data preparation and model configuration will be different. This study also uses the Design Science Research Methodology (DSRM) for research as a whole before focusing on the use of the SVM method. The results of the study consist of an algorithm for creating data sets and a classification model for sentiment analysis that can be used to determine public perceptions of the issue of Covid-19 vaccination. This study also compares the use of unigram and bigram tokenization. Based on the results obtained, the average value of each aspect of the evaluation measurement is higher when the bigram tokenization is used. Although higher, the value obtained has an insignificant difference in the range of 0.6% - 0.7%. In the evaluation results using unigram and bigram tokenization, the highest scores for all aspects of measurement, namely accuracy, recall, f-measure, and precision were 84%
INFORMATION RETRIEVAL BERBASIS LATENT DIRICHLET ALLOCATION PADA DATA KEKAYAAN INTELEKTUAL
Full text linkThe shift toward a knowledge-based economy underscores the importance of intellectual property (IP) management. Unfortunately, conventional keyword-based search methods often fail to capture the semantic relationships between concepts in documents—particularly complex ones like patents and copyrights. This study proposes a topic modeling approach using the Latent Dirichlet Allocation (LDA) method to improve the relevance and accuracy of information retrieval in IP data. The research developed 76 models based on four scenarios: with and without language translation, and with and without n-gram tokenization, using topic numbers ranging from 1 to 19. The best four models from each scenario yielded coherence scores between 0.4411 and 0.4581. Evaluation using Mean Average Precision (MAP) on the top 10 documents showed that the model without translation and with unigram tokenization (10 topics) achieved the best results with an average MAP of 78%. The findings indicate that language translation and n-gram tokenization do not significantly impact the coherence score. However, models without n-gram tokenization (bigram and trigram combinations) yielded relatively more semantically relevant search results based on MAP values. Automatic translation in this study resulted in lower MAP scores compared to models without translation
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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