Artículos de revistas: "Twitter data analytics"

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Gukanesh, A. V., G. Karthick Kumar y K. Karthik Raja Kumar N. Saranya. "Twitter Data Analytics – Sentiment Analysis of An Election". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 1600–1603. http://dx.doi.org/10.31142/ijtsrd11457.

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Negara, Edi Surya, Ria Andryani y Prihambodo Hendro Saksono. "Analisis Data Twitter: Ekstraksi dan Analisis Data G eospasial". Jurnal INKOM 10, n.º 1 (21 de noviembre de 2016): 27. http://dx.doi.org/10.14203/j.inkom.433.

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Data geospasial pada media sosial Twitter dapat dimanfaatkan untuk mengetahui informasi spasial (lokasi) yang merupakan lokasi sumber munculnya persepsi publik terhadap sebuah isu di media sosial. Besarnya produksi data geospasial yang dihasilkan oleh Twitter memberikan peluang besar untuk dapat dimanfaatkan oleh berbagai pihak sehingga menghasilkan informasi yang lebih bernilai melalui proses Twitter Data Analytics. Proses pemanfaatan data geospasial Twitter dimulai dengan melakukan proses ekstraksi terhadap informasi spatial berupa titik koordinat pengguna Twitter. Titik koordinat pengguna Twitter didapatkan dari sharing location yang dilakukan oleh pengguna Twitter. Untuk mengekstrak dan menganalisis data geospasial pada Twitter dibutuhkan pengetahuan dan kerangka kerja tentang social media analytics (SMA). Pada penelitian ini dilakukan ekstraksi dan analisis data geospasial Twitter terhadap suatu isu publik yang sedang berkembang dan mengembangakan prototipe perangkat lunak yang digunakan untuk mendapatkan data geospasial yang ada pada Twitter. Proses ekstraksi dan analisis dilakukan melalui empat tahapan yaitu: proses penarikan data (crawling), penyimpanan (storing), analisis (analyzing), dan visualisasi (vizualizing). Penelitian ini bersifat exploratory yang terfokus pada pengembangan teknik ekstrasi dan analisis terhadap data geospasial twitter

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Hoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh y Radhika Gopi. "Visual Twitter Analytics (Vista)". Online Information Review 40, n.º 1 (8 de febrero de 2016): 25–41. http://dx.doi.org/10.1108/oir-02-2015-0067.

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Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes. Findings – A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori. Originality/value – Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.

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Et. al., Vedant Karmalkar,. "Twego Trending: Data Analytics Based Search Engine Using Elasticsearch". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, n.º 1S (11 de abril de 2021): 246–51. http://dx.doi.org/10.17762/turcomat.v12i1s.1764.

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Twitter monitoring enables firms to consider their market, stay on track of what is being said regarding their company and contenders, and uncover emerging market trends. Twego Trending is a platform where data will be viewed and structured by an automated procedure of analyzing and processing tweets data and classifying it into various hash statistics and visualizations. Implementing Twego forecasting analysis on Twitter data using various technologies may help businesses know how consumers talk about their product. Twitter has more than 340 million active users and almost 500 millions tweets are posted every day. This social media platform helps companies to reach a large audience and communicate without intermediaries with consumers. The aim is to build a Search Engine in which , when someone will type in a query , it will return back tweets as well as do data analytics on the results and provide visualizations.

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Al-Ibrahim, Fatimah y Zakarya A. Alzamil. "Big Data Contextual Analytics Study on Arabic Tweets Summarization". International Journal of Knowledge and Systems Science 10, n.º 4 (octubre de 2019): 18–34. http://dx.doi.org/10.4018/ijkss.2019100102.

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Twitter represents a source of information as well as a free space for people to express their opinions on diverse topics. The use of twitter is rapidly increasing and generates a massive amount of data from several types and forms, in which searching for relevant tweets in a specific topic is hard manually due to irrelevant tweets. There has been much research on English tweets for understanding context; however, in spite of the fact that the Twitter active Arabic users are over hundreds of millions, there are very limited studies that have investigated Arabic tweets to produce an automatic summarization. This article proposes a multi-conversational Arabic tweets summarization approach, with a new concept of tweet classification based on influence factor. Such an approach is able to analyze Arabic tweets and provide a readable, informative, precise, concise, and diversified summary. The evaluation metrics of precision, recall, and f-measure have shown good results of the system compared to related Arabic summarization studies.

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Lee, George, Jimmy Lin, Chuang Liu, Andrew Lorek y Dmitriy Ryaboy. "The unified logging infrastructure for data analytics at Twitter". Proceedings of the VLDB Endowment 5, n.º 12 (agosto de 2012): 1771–80. http://dx.doi.org/10.14778/2367502.2367516.

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Haghighati, Amir y Kamran Sedig. "VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data". Data 5, n.º 1 (19 de febrero de 2020): 20. http://dx.doi.org/10.3390/data5010020.

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Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.

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Rodrigues, Anisha P., Roshan Fernandes, Adarsh Bhandary, Asha C. Shenoy, Ashwanth Shetty y M. Anisha. "Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques". Wireless Communications and Mobile Computing 2021 (25 de octubre de 2021): 1–13. http://dx.doi.org/10.1155/2021/3920325.

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Twitter is a popular microblogging social media, using which its users can share useful information. Keeping a track of user postings and common hashtags allows us to understand what is happening around the world and what are people’s opinions on it. As such, a Twitter trend analysis analyzes Twitter data and hashtags to determine what topics are being talked about the most on Twitter. Feature extraction and trend detection can be performed using machine learning algorithms. Big data tools and techniques are needed to extract relevant information from continuous steam of data originating from Twitter. The objectives of this research work are to analyze the relative popularity of different hashtags and which field has the maximum share of voice. Along with this, the common interests of the community can also be determined. Twitter trends plan an important role in the business field, marketing, politics, sports, and entertainment activities. The proposed work implemented the Twitter trend analysis using latent Dirichlet allocation, cosine similarity, K means clustering, and Jaccard similarity techniques and compared the results with Big Data Apache SPARK tool implementation. The LDA technique for trend analysis resulted in an accuracy of 74% and Jaccard with an accuracy of 83% for static data. The results proved that the real-time tweets are analyzed comparatively faster in the Big Data Apache SPARK tool than in the normal execution environment.

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Sholehurrohman, Ridho y Igit Sabda Ilman. "ANALISIS SENTIMEN TWEET KASUS KEBOCORAN DATA PENGGUNAAN FACEBOOK OLEH CAMBRIGDE ANALYTICA". Jurnal Pepadun 3, n.º 1 (1 de abril de 2022): 140–47. http://dx.doi.org/10.23960/pepadun.v3i1.108.

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The case of the Facebook user data leak by Cambridge Analytica has been spotlight in the public lately. Many of the citizens has participated discussing this case, especially in social media Twitter. Sentiment analysis is a computational research of opinions and emotions sentiment that are expressed textually. This study aims to classify positive and negative sentiment from Twitter data and to determine the accuracy of the classification model using Naïve Bayes Classifier method. Based on experiment conducted by tweet data with the “Zuckerberg” and “Cambridge Analytics” keywords, it has been produced Naïve Bayes Classifier with an accuracy of 83.06%.

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Chae, Bongsug (Kevin). "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research". International Journal of Production Economics 165 (julio de 2015): 247–59. http://dx.doi.org/10.1016/j.ijpe.2014.12.037.

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Razis, Gerasimos, Georgios Theofilou y Ioannis Anagnostopoulos. "Latent Twitter Image Information for Social Analytics". Information 12, n.º 2 (21 de enero de 2021): 49. http://dx.doi.org/10.3390/info12020049.

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The appearance of images in social messages is continuously increasing, along with user engagement with that type of content. Analysis of social images can provide valuable latent information, often not present in the social posts. In that direction, a framework is proposed exploiting latent information from Twitter images, by leveraging the Google Cloud Vision API platform, aiming at enriching social analytics with semantics and hidden textual information. As validated by our experiments, social analytics can be further enriched by considering the combination of user-generated content, latent concepts, and textual data extracted from social images, along with linked data. Moreover, we employed word embedding techniques for investigating the usage of latent semantic information towards the identification of similar Twitter images, thereby showcasing that hidden textual information can improve such information retrieval tasks. Finally, we offer an open enhanced version of the annotated dataset described in this study with the aim of further adoption by the research community.

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Arifin, Firman, Budi Nur Iman, Budi Nur Iman, Elly Purwantini, Elly Purwantini, Mochamad Hariadi, Mochamad Hariadi, Muhammad Anshari y Muhammad Anshari. "Data Analytics to Examine Trending Topics for Indonesian Election 2019". INOVTEK Polbeng - Seri Informatika 4, n.º 2 (28 de noviembre de 2019): 235. http://dx.doi.org/10.35314/isi.v4i2.984.

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Understanding public interest and opinion are necessary tasks in high intense political competition. Utilizing big data analytics from social media provide an important source of information that candidates can utilize, manage and even engage them in targeted political campaigning agenda. One of the source in big data is social media’s interactions. Social media empowers public to participate proactivelyin the campaigning activities. This paper examines trends gathered from data analytics of two contenders’ group for Indonesian Election in 2019. It tracks the recent patterns of people engagement via social media analytic specifically Twitter. The study developed the analysis into the proposed model based on their trends and patterns.

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He, Wu, Weidong Zhang, Xin Tian, Ran Tao y Vasudeva Akula. "Identifying customer knowledge on social media through data analytics". Journal of Enterprise Information Management 32, n.º 1 (11 de febrero de 2019): 152–69. http://dx.doi.org/10.1108/jeim-02-2018-0031.

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Purpose Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management. Design/methodology/approach The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge. Findings The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers. Originality/value This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

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Udanor, Collins, Stephen Aneke y Blessing Ogechi Ogbuokiri. "Determining social media impact on the politics of developing countries using social network analytics". Program 50, n.º 4 (6 de septiembre de 2016): 481–507. http://dx.doi.org/10.1108/prog-02-2016-0011.

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Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms. Findings Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies. Practical implications With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics. Social implications Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB. Originality/value The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

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Iftikhar, Rehan y Mohammad Saud Khan. "Social Media Big Data Analytics for Demand Forecasting". Journal of Global Information Management 28, n.º 1 (enero de 2020): 103–20. http://dx.doi.org/10.4018/jgim.2020010106.

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Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

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Harfoushi, Osama, Dana Hasan y Ruba Obiedat. "Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison". Modern Applied Science 12, n.º 7 (21 de junio de 2018): 49. http://dx.doi.org/10.5539/mas.v12n7p49.

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The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

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Ciasullo, Maria Vincenza, Orlando Troisi, Francesca Loia y Gennaro Maione. "Carpooling: travelers’ perceptions from a big data analysis". TQM Journal 30, n.º 5 (13 de agosto de 2018): 554–71. http://dx.doi.org/10.1108/tqm-11-2017-0156.

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Purpose The purpose of this paper is to provide a better understanding of the reasons why people use or do not use carpooling. A further aim is to collect and analyze empirical evidence concerning the advantages and disadvantages of carpooling. Design/methodology/approach A large-scale text analytics study has been conducted: the collection of the peoples’ opinions have been realized on Twitter by means of a dedicated web crawler, named “Twitter4J.” After their mining, the collected data have been treated through a sentiment analysis realized by means of “SentiWordNet.” Findings The big data analysis identified the 12 most frequently used concepts about carpooling by Twitter’s users: seven advantages (economic efficiency, environmental efficiency, comfort, traffic, socialization, reliability, curiosity) and five disadvantages (lack of effectiveness, lack of flexibility, lack of privacy, danger, lack of trust). Research limitations/implications Although the sample is particularly large (10 percent of the data flow published on Twitter from all over the world in about one year), the automated collection of people’s comments has prevented a more in-depth analysis of users’ thoughts and opinions. Practical implications The research findings may direct entrepreneurs, managers and policy makers to understand the variables to be leveraged and the actions to be taken to take advantage of the potential benefits that carpooling offers. Originality/value The work has utilized skills from three different areas, i.e., business management, computing science and statistics, which have been synergistically integrated for customizing, implementing and using two IT tools capable of automatically identifying, selecting, collecting, categorizing and analyzing people’s tweets about carpooling.

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Lou, Shanshan. "Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study". Journal of Advertising Education 21, n.º 1 (mayo de 2017): 26–32. http://dx.doi.org/10.1177/109804821702100106.

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This paper presents a class case study of an assignment that asked students to use a Twitter follower report to design a Twitter advertising campaign. The purpose of this case study is to immerse students in a real social media environment and help them become familiar with analyzing social media data to develop advertising campaigns. Students' interview responses suggest that incorporating a project that requires social media analytics techniques in an advertising class can help them better understand the role of secondary research and database analysis in developing consumer profiles and making campaign decisions. The findings also suggest that students have a strong desire to work with secondary data in designing social media advertising campaigns. The advantages of data analytics should be further explored in advertising campaign classes to help students become successful campaign designers. Limitations and future research direction are also discussed.

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Awan, Mazhar Javed, Awais Yasin, Haitham Nobanee, Ahmed Abid Ali, Zain Shahzad, Muhammad Nabeel, Azlan Mohd Zain y Hafiz Muhammad Faisal Shahzad. "Fake News Data Exploration and Analytics". Electronics 10, n.º 19 (23 de septiembre de 2021): 2326. http://dx.doi.org/10.3390/electronics10192326.

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Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

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Aljojo, Nahla. "Examining Heterogeneity Structured on a Large Data Volume with Minimal Incompleteness". ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, n.º 2 (2 de noviembre de 2021): 30–37. http://dx.doi.org/10.14500/aro.10857.

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While Big Data analytics can provide a variety of benefits, processing heterogeneous data comes with its own set of limitations. A transaction pattern must be studied independently while working with Bitcoin data, this study examines twitter data related to Bitcoin and investigate communications pattern on bitcoin transactional tweet. Using the hashtags #Bitcoin or #BTC on Twitter, a vast amount of data was gathered, which was mined to uncover a pattern that everyone either (speculators, teaches, or the stakeholders) uses on Twitter to discuss Bitcoin transactions. This aim is to determine the direction of Bitcoin transaction tweets based on historical data. As a result, this research proposes using Big Data analytics to track Bitcoin transaction communications in tweets in order to discover a pattern. Hadoop platform MapReduce was used. The finding indicate that In the map step of the procedure, Hadoop's tokenize the dataset and parse them to the mapper where thirteen patterns were established and reduced to three patterns using the attributes previously stored data in the Hadoop context, one of which is the Emoji data that was left out in previous research discussions, but the text is only one piece of the puzzle on bitcoin transaction interaction, and the key part of it is “No certainty, only possibilities” in Bitcoin transactions

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Manan koli, Abdul, Muqeem Ahmed y . "Election Prediction Using Big Data Analytics-A Survey". International Journal of Engineering & Technology 7, n.º 4.5 (22 de septiembre de 2018): 366. http://dx.doi.org/10.14419/ijet.v7i4.5.20108.

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Social media has received much attention due to it's real-time and interactive nature for political discourse, especially around election times. Recently studies have explored the power of social media platforms such as Twitter or Facebook, on recording current social trends and predicting the voting outcomes of an area. These social media generate a large amount of raw data that can be used in decision making for election predictions. This tremendously generated data is referred to as “Big data”. After scrutinized a lot of research work related to election prediction, a survey paper is presented in which every work related to election prediction using social media is incorporated. This paper is an attempt to review various tools, models, and algorithms used for the observation of campaign, discussion, prediction, and analysis of the election, and also suggest further tools and techniques for improvement.

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Mazumdar, Suvodeep y Dhavalkumar Thakker. "Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks". Future Internet 12, n.º 12 (26 de noviembre de 2020): 210. http://dx.doi.org/10.3390/fi12120210.

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This paper presents a long-term study on how the public engage with discussions around citizen science and crowdsourcing topics. With progress in sensor technologies and IoT, our cities and neighbourhoods are increasingly sensed, measured and observed. While such data are often used to inform citizen science projects, it is still difficult to understand how citizens and communities discuss citizen science activities and engage with citizen science projects. Understanding these engagements in greater depth will provide citizen scientists, project owners, practitioners and the generic public with insights around how social media can be used to share citizen science related topics, particularly to help increase visibility, influence change and in general and raise awareness on topics. To the knowledge of the authors, this is the first large-scale study on understanding how such information is discussed on Twitter, particularly outside the scope of individual projects. The paper reports on the wide variety of topics (e.g., politics, news, ecological observations) being discussed on social media and a wide variety of network types and the varied roles played by users in sharing information in Twitter. Based on these findings, the paper highlights recommendations for stakeholders for engaging with citizen science topics.

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Gupta, Vibhuti y Rattikorn Hewett. "Real-Time Tweet Analytics Using Hybrid Hashtags on Twitter Big Data Streams". Information 11, n.º 7 (30 de junio de 2020): 341. http://dx.doi.org/10.3390/info11070341.

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Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolving nature, lack of context, slang, abbreviations, and non-standardized expression by users. Most existing approaches, which utilize hashtags for tweet topic classification, focus on extracting hashtag concepts from external lexicon resources to derive semantics. However, due to the rapid evolution and non-standardized expression of hashtags, the majority of these lexicon resources either suffer from the lack of hashtag words in their knowledge bases or use multiple resources at once to derive semantics, which make them unscalable. Along with scalable and automated techniques for tweet topic classification using hashtags, there is also a requirement for real-time analytics approaches to handle huge and dynamic flows of textual streams generated by Twitter. To address these problems, this paper first presents a novel semi-automated technique that derives semantically relevant hashtags using a domain-specific knowledge base of topic concepts and combines them with the existing tweet-based-hashtags to produce Hybrid Hashtags. Further, to deal with the speed and volume of Big Data streams of tweets, we present an online approach that updates the preprocessing and learning model incrementally in a real-time streaming environment using the distributed framework, Apache Storm. Finally, to fully exploit the batch and stream environment performance advantages, we propose a comprehensive framework (Hybrid Hashtag-based Tweet topic classification (HHTC) framework) that combines batch and online mechanisms in the most effective way. Extensive experimental evaluations on a large volume of Twitter data show that the batch and online mechanisms, along with their combination in the proposed framework, are scalable, efficient, and provide effective tweet topic classification using hashtags.

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Mukesh Kumar Sah., Rishabh Sharma & Amritpal Singh. "Real Time Data Pipeline for Twitter Trends Analysis". International Journal for Modern Trends in Science and Technology 7, n.º 05 (6 de mayo de 2021): 44–48. http://dx.doi.org/10.46501/ijmtst0705006.

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In social media, Information is present in enormous amount. Extracting data from processed information from social media gives us diverse usages in various fields. In the field of Business Analytics, HealthCare, Technologies and Trending Topics in Social Media posted by the user. Extracting information from social media is providing number of benefits such as knowledge about the latest Technology, Medical field, Business Decisions, etc. Twitter is solitary of the social media which allows the user post tweets of limited number of characters and share the tweet to their followers. Twitter allows application developer to access the tweets for their motive. In the implemented methodology, Tweets are collected, and sentiment analysis is performed on them. Based on the results of sentimental analysis of Trending Topics in Twitter, suggestions can be provided to the user. In this way, the implemented system can help in improving the growth of business, healthcare, technologies and alsoNegative or Positive mentions of a product or service can be determined.

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Friedman, Alon y Martin Thellefsen. "Big data visualization through the lens of Peirce’s visual sign theory". Punctum. International Journal of Semiotics 08, n.º 01 (2022): 115–36. http://dx.doi.org/10.18680/hss.2022.0007.

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Data from social media platforms, such as Twitter and Facebook, are generated by people who produce, spread, share, or exchange multimedia content. Such content may include text, images, sounds, or videos. To derive insight into the behavior of social media users, researchers often use open-source technologies to visualize data and generate models for data analytics. One of the most popular open-source applications for managing and analyzing social media data is the open-source R programming language. Friedman and Feichtinger (2017) created an R package termed ‘Peirce’s sign theory R package’ to analyze data using Peirce’s principles of discovery. Though Peirce semiotics have been introduced in the context of computer programming languages, so far, no previous work has applied Peirce’s sign theory to data modelling of social media data. In this paper, we use Peirce’s sign theory R package as an overall framework to gain insight into data collected from Twitter. We assembled the data using Twitter’s Analytics algorithm, examined the relationships between variables, and visualized the results. Subsequently, we assessed the feasibility of analyzing those graphics using the triadic model set out by Jappy (2013) and Peirtarinen (2012) for the interpretation of visual signs. The study results showed that Peirce’s sign theory R package effectively analyzes and visualizes Big Data from social media feeds. However, due to complexities in both the social media data feeds and Peirce’s interpretation of meaning, as outlined by Jappy (2013) and Peirtarinen (2012), we were unable to develop algorithms that generate or suggest an interpretation of visual signs.

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Balan, Shilpa y Janhavi Rege. "Mining for Social Media: Usage Patterns of Small Businesses". Business Systems Research Journal 8, n.º 1 (28 de marzo de 2017): 43–50. http://dx.doi.org/10.1515/bsrj-2017-0004.

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AbstractBackground: Information can now be rapidly exchanged due to social media. Due to its openness, Twitter has generated massive amounts of data. In this paper, we apply data mining and analytics to extract the usage patterns of social media by small businesses. Objectives: The aim of this paper is to describe with an example how data mining can be applied to social media. This paper further examines the impact of social media on small businesses. The Twitter posts related to small businesses are analyzed in detail. Methods/Approach: The patterns of social media usage by small businesses are observed using IBM Watson Analytics. In this paper, we particularly analyze tweets on Twitter for the hashtag #smallbusiness. Results: It is found that the number of females posting topics related to small business on Twitter is greater than the number of males. It is also found that the number of negative posts in Twitter is relatively low. Conclusions: Small firms are beginning to understand the importance of social media to realize their business goals. For future research, further analysis can be performed on the date and time the tweets were posted.

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Pradana, Meirza Luthfi, Vincencius Alvian Pratama, Rika Aulia Ramdhani y Panji Putranto Nugrahagung. "Memaknai TWK KPK Dalam Reproduksi Wacana Dengan Pemanfaatan Modal Sosial: Studi Kasus Data Percakapan TWK KPK di Media Sosial Twitter". Jurnal PolGov 4, n.º 2 (30 de diciembre de 2022): 1–49. http://dx.doi.org/10.22146/polgov.v4i2.3637.

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Kajian ini bertujuan untuk menganalisis pemetaan aktor dan pola sentimen yang terbentuk di media sosial Twitter dalam wacana isu “Tes Wawasan Kebangsaan Komisi Pemberantasan Korupsi (TWK KPK)”. Artikel ini menggunakan pendekatan mix method, yakni kualitatif dan kuantitatif, dengan menggunakan pisau analisis Social Network Analysis (SNA) . Data dalam kajian ini diperoleh dari media sosial Twitter yang diolah dengan Big Data Analytics dari tanggal 16 Mei 2021 sampai dengan 13 September 2021. Tulisan ini mengungkap terdapat tiga akun eks pejabat publik dan dua akun media nasional yang menjadi top engagement pada isu TWK KPK di Twitter. Adapun sentimen-sentimen mengenai isu TWK KPK yang terbentuk, meliputi isu positif, negatif, dan netral.

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Grasso, Valentina, Alfonso Crisci, Marco Morabito, Paolo Nesi, Gianni Pantaleo, Imad Zaza y Bernardo Gozzini. "Italian codified hashtags for weather warning on Twitter – who is really using them?" Advances in Science and Research 14 (4 de abril de 2017): 63–69. http://dx.doi.org/10.5194/asr-14-63-2017.

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Abstract. During emergencies, an increasing number of messages are shared through social media platforms, becoming a primary source of information for lay people and emergency managers. Weather services and institutions have started to employ social media to deliver weather warnings even if sometimes this communication lacks in strategy. In Twitter, for example, hashtagging is very important to associate messages with certain topics; in recent years, codified hashtagging is emerging as a practical way to coordinate Twitter conversations during emergencies and quickly retrieve relevant information. In 2014, a syntax for codified hashtags for weather warning was proposed in Italy: a list of 20 hashtags, realized by combining #allertameteo (weather warning) + XXX, where final letters code the regional identification. This contribution presents a monitoring of Twitter usage of weather warning codified hashtags in Italy (since July 2015) and an analysis of different contexts. Twitter messages were retrieved using TwitterVigilance, a multi-users platform to crawl Twitter data, collect and store messages and perform quantitative analytics, about users, hashtags, tweets/retweets volumes. The Codified Hashtags data set is presented and discussed with main analytics and evaluation of regional contexts where it was successfully employed.

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Tang, LiYaning, Yiming Zhang, Fei Dai, Yoojung Yoon, Yangqiu Song y Radhey S. Sharma. "Social Media Data Analytics for the U.S. Construction Industry: Preliminary Study on Twitter". Journal of Management in Engineering 33, n.º 6 (noviembre de 2017): 04017038. http://dx.doi.org/10.1061/(asce)me.1943-5479.0000554.

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Queiroz, Maciel M. "A framework based on Twitter and big data analytics to enhance sustainability performance". Environmental Quality Management 28, n.º 1 (septiembre de 2018): 95–100. http://dx.doi.org/10.1002/tqem.21576.

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Löchner, Marc y Dirk Burghardt. "Using HyperLogLog to Prevent Data Retention in Social Media Streaming Data Analytics". ISPRS International Journal of Geo-Information 12, n.º 2 (9 de febrero de 2023): 60. http://dx.doi.org/10.3390/ijgi12020060.

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Social media data are widely used to gain insights about social incidents, whether on a local or global scale. Within the process of analyzing and evaluating the data, it is common practice to download and store it locally. Considerations about privacy protection of social media users are often neglected thereby. However, protecting privacy when dealing with personal data is demanded by laws and ethics. In this paper, we introduce a method to store social media data using the cardinality estimator HyperLogLog. Based on an exemplary disaster management scenario, we show that social media data can be analyzed by counting occurrences of posts, without becoming in possession of the actual raw data. For social media data analyses like these, that are based on counting occurrences, cardinality estimation suffices the task. Thus, the risk of abuse, loss, or public exposure of the data can be mitigated and privacy of social media users can be preserved. The ability to do unions and intersections on multiple datasets further encourages the use of this technology. We provide a proof-of-concept implementation for our introduced method, using data provided by the Twitter API.

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Walker, Rick, Llyr ap Cenydd, Serban Pop, Helen C. Miles, Chris J. Hughes, William J. Teahan y Jonathan C. Roberts. "Storyboarding for visual analytics". Information Visualization 14, n.º 1 (28 de mayo de 2013): 27–50. http://dx.doi.org/10.1177/1473871613487089.

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Analysts wish to explore different hypotheses, organize their thoughts into visual narratives and present their findings. Some developers have used algorithms to ascertain key events from their data, while others have visualized different states of their exploration and utilized free-form canvases to enable the users to develop their thoughts. What is required is a visual layout strategy that summarizes specific events and allows users to layout the story in a structured way. We propose the use of the concept of ‘storyboarding’ for visual analytics. In film production, storyboarding techniques enable film directors and those working on the film to pre-visualize the shots and evaluate potential problems. We present six principles of storyboarding for visual analytics: composition, viewpoints, transition, annotability, interactivity and separability. We use these principles to develop epSpread, which we apply to VAST Challenge 2011 microblogging data set and to Twitter data from the 2012 Olympic Games. We present technical challenges and design decisions for developing the epSpread storyboarding visual analytics tool that demonstrate the effectiveness of our design and discuss lessons learnt with the storyboarding method.

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Mane, Deepak, Dr Sirbi Kotrappa y Kiran Shibe. "Sentiment Analytics on Chinese Product Boycott from Multiple Data Sources". Computational Intelligence and Machine Learning 2, n.º 1 (20 de abril de 2021): 16–25. http://dx.doi.org/10.36647/ciml/02.01.a003.

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Sentiment Analysis and Opinion mining is a technique recognizing and drawing out the personalized information underlying a different kind of documents such as text, audio, images and videos. This area of research tries to exaplain the feeling, opinions, emotions of people on something topics. The most relevant classifying a statement as ‘positive’ , ‘negative’ and ‘neutral’ from records/posts obtained from different source system such as Twitter, Facebook , Reddit etc. To predict the sentiment/result of recent Chinese Product Boycott campaign, This paper direct to operate on data received from 9 different sources. In the field of Trade and commerce where traders. Politians and Peoples need to catch public’s point of view, thinking and therefor evaluate people’s reaction about Chinese product. The reasoning behind performing this research is that, the prediction will also help to know what is reason behind this , Chinese product boycott analysis will have a major impact on relationship between India and China trade. Keyword : Sentiment, Chinese Product, Data Sources, Boycott

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Park, Seunghyun Brian, Jichul Jang y Chihyung Michael Ok. "Analyzing Twitter to explore perceptions of Asian restaurants". Journal of Hospitality and Tourism Technology 7, n.º 4 (14 de noviembre de 2016): 405–22. http://dx.doi.org/10.1108/jhtt-08-2016-0042.

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Purpose The purpose of this paper is to use Twitter analysis to explore diner perceptions of four types of Asian restaurants (Chinese, Japanese, Korean and Thai). Design/methodology/approach Using 86,015 tweets referring to Asian restaurants, this research used text mining and sentiment analysis to find meaningful patterns, popular words and emotional states in opinions. Findings Twitter users held mingled perceptions of different types of Asian restaurants. Sentiment analysis and ANOVA showed that the average sentiment scores for Chinese restaurants was significantly lower than the other three Asian restaurants. While most positive tweets referred to food quality, many negative tweets suggested problems associated with service quality or food culture. Research limitations/implications This research provides a methodology that future researchers can use in applying social media analytics to explore major issues and extract sentiment information from text messages. Originality/value Limited research has been conducted applying social media analysis in hospitality research. This study fills a gap by using social media analytics with Twitter data to examine the Twitter users’ thoughts and emotions for four different types of Asian restaurants.

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Chae, Bongsug (Kevin) y Gyuhyeong Goh. "Digital Entrepreneurs in Artificial Intelligence and Data Analytics: Who Are They?" Journal of Open Innovation: Technology, Market, and Complexity 6, n.º 3 (29 de julio de 2020): 56. http://dx.doi.org/10.3390/joitmc6030056.

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Digital technologies are key resources for entrepreneurial activities and there is great interest in digital entrepreneurship. While much research has focused on the role of digital technologies in entrepreneurship and how they are shaping the field, there has been relatively little research on those key players of digital entrepreneurship. Using data from Crunchbase and Twitter API and a learning machine, this study attempts to answer the question of “who are digital entrepreneurs?” This study reports that digital entrepreneurs in the artificial intelligence and data analytics (AIDA) industry are more likely to be male and to be active and connected online than non-digital entrepreneurs. In addition, they tend to be more extroverted and less conscientious and agreeable than other, non-digital, entrepreneurs. Our findings help to develop a clearer picture of digital entrepreneurs, which would be of great interest to investors, policy makers, current and future digital entrepreneurs and educators.

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Baharuddin, Tawakkal, Salahudin Salahudin, Sjafri Sairin, Zuly Qodir y Hasse Jubba. "Kampanye Antikorupsi Kaum Muda melalui Media Sosial Twitter". Jurnal Ilmu Komunikasi 19, n.º 1 (1 de mayo de 2021): 58. http://dx.doi.org/10.31315/jik.v19i1.3827.

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Kaum Muda masih dinilai sebagai kelompok yang apatis di berbagai Negara termasuk di Indonesia. Situasi ini dikarenakan kurangnya ruang partisipatif yang mengakomodir kepentingan Kaum Muda. Kehadiran media sosial perlahan memberikan ruang partisipasi kreatif baru bagi Kaum Muda. Penelitian ini bertujuan untuk menganalisis penggunaan media sosial Twitter sebagai media partisipasi kreatif dan ekspresi politik Kaum Muda melawan korupsi di Indonesia. Penelitian ini menggunakan pendekatan kuantitatif dengan sumber data berasal dari studi dokumen dan media sosial Twitter. Data dikumpulkan menggunakan fitur Ncapture for Nvivo. Analisis penelitian dilakukan dengan pengkodean data, analisis konten dan visualisasi data menggunakan Software analytics Nvivo 12 Plus. Hasil penelitian ini menjelaskan bahwa media sosial Twitter memiliki pengaruh pada minat kolektif Kaum Muda pada wacana politik khususnya masalah korupsi. Ekspresi Kaum Muda di Twitter dibuktikan dengan ide kreativitas seperti meme, capture, caption, quote, dan hastag. Kreativitas tersebut merupakan bentuk ekspresi politik yang sekaligus mampu memengaruhi dan memobilisasi pengguna media sosial lainnya untuk ikut terlibat pada minat kolektif bersama melawan korupsi. Substansi penelitian ini memberikan kontribusi berupa rekomendasi konsep baru dalam mengampanyekan isu-isu antikorupsi di Indonesia dengan memaksimalkan penggunaan media sosial Twitter.

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Lynn, Theo, Pierangelo Rosati, Binesh Nair y Ciáran Mac an Bhaird. "An Exploratory Data Analysis of the #Crowdfunding Network on Twitter". Journal of Open Innovation: Technology, Market, and Complexity 6, n.º 3 (11 de septiembre de 2020): 80. http://dx.doi.org/10.3390/joitmc6030080.

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Together, social media and crowdsourcing can help entrepreneurs to attract external finance and early-stage customers. This paper investigates the characteristics and discourse of an issue-centered public on Twitter organized around the hashtag #crowdfunding through the lens of social network theory. Using a dataset of 2,732,144 tweets published during a calendar year, we use exploratory data analysis to generate insights and hypotheses on who the users in the #crowdfunding network are, what they share, and how they are connected to each other. In order to do so, we adopt a range of descriptive, content, network analytics techniques. The results suggest that platforms, crowdfunders, and other actors who derive income from the crowdfunding economy play a key role in creating the network. Furthermore, latent ties (strangers) play a direct role in disseminating information, investing, and sending signals to platforms that further raises campaign prominence. We also introduce a new type of social tie, the “computer as a social actor”, previously unaddressed in entrepreneurial network literature, which play a role in sending signals to both platforms and networks. Our results suggest that homophily is a key driver for creating network sub-communities built around specific platforms, project types, domains, or geography.

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Larasati, Aisyah, Raretha Maren y Retno Wulandari. "Utilizing Elbow Method for Text Clustering Optimization in Analyzing Social Media Marketing Content of Indonesian e-Commerce". Jurnal Teknik Industri 23, n.º 2 (21 de diciembre de 2021): 111–20. http://dx.doi.org/10.9744/jti.23.2.111-120.

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The massive increases in textual data from Twitter and text analytics simultaneously have driven organizations to obtain hidden insights to implement the proper marketing strategies for businesses. The vast information generated by Twitter enables most e-commerce businesses to utilize Twitter to implement social media marketing. One of those e-commerce businesses is Blibli Indonesia. Intense business competition has led them to perform marketing strategies to understand consumer tendencies. Focusing the marketing strategies on consumer preferences enables the increase of consumer interest in Blibli, which is in line with enhancing the opportunity to reach new consumers. This research aims to discover Twitter content based on k-means results to cluster the tweets of @bliblidotcom. The best cluster is determined with the elbow method by selecting the deepest curvature, three clusters. The result suggests that Twitter users like Park Seo Jun's content. Hence, Blibli can focus on that content as its business marketing strategy on the Twitter platform.

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Hiriyannaiah, Srinidhi, Siddesh G.M. y Srinivasa K.G. "Predictive Analytical Model for Microblogging Data Using Asset Bubble Modelling". International Journal of Cognitive Informatics and Natural Intelligence 14, n.º 2 (abril de 2020): 108–18. http://dx.doi.org/10.4018/ijcini.2020040107.

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In recent days, social media plays a significant role in the ecosystem of the big data world and its different types of information. There is an emerging need for collection, monitoring, analyzing, and visualizing the different information from various social media platforms in different domains like businesses, public administration, and others. Social media acts as the representative with numerous microblogs for analytics. Predictive analytics of such microblogs provides insights into various aspects of the real-world entities. In this article, a predictive model is proposed using the tweets generated on Twitter social media. The proposed model calculates the potential of a topic in the tweets for the prediction purposes. The experiments were conducted on tweets of the regional election in India and the results are better than the existing systems. In the future, the model can be extended for analysis of information diffusion in heterogeneous systems.

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Roy, Sudipta, Mamta Mittal, Tai Hoon Kim, Kushal Dhayal, Debnath Bhattacharyya y Bhavya Patel. "Demographical gender prediction of Twitter users using big data analytics: an application of decision marketing". International Journal of Reasoning-based Intelligent Systems 13, n.º 2 (2021): 41. http://dx.doi.org/10.1504/ijris.2021.10036801.

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Roy, Sudipta, Bhavya Patel, Debnath Bhattacharyya, Kushal Dhayal, Tai Hoon Kim y Mamta Mittal. "Demographical gender prediction of Twitter users using big data analytics: an application of decision marketing". International Journal of Reasoning-based Intelligent Systems 13, n.º 2 (2021): 41. http://dx.doi.org/10.1504/ijris.2021.114629.

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42

Shirdastian, Hamid, Michel Laroche y Marie-Odile Richard. "Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter". International Journal of Information Management 48 (octubre de 2019): 291–307. http://dx.doi.org/10.1016/j.ijinfomgt.2017.09.007.

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Sim, Jisoo y Patrick Miller. "Understanding an Urban Park through Big Data". International Journal of Environmental Research and Public Health 16, n.º 20 (10 de octubre de 2019): 3816. http://dx.doi.org/10.3390/ijerph16203816.

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To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

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Best, Daniel, Joseph Bruce, Scott Dowson, Oriana Love y Liam McGrath. "Web-Based Visual Analytics for Social Media". Proceedings of the International AAAI Conference on Web and Social Media 6, n.º 4 (3 de agosto de 2021): 2–5. http://dx.doi.org/10.1609/icwsm.v6i4.14363.

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Social media provides a rich source of data that reflects current trends on a multitude of topics. The data can be harvested from Twitter, Facebook, blogs, and other social applications. The high rate of adoption of social media has created a domain that is difficult to analyze, due to the ever-expanding volume of data. Information visualization is key in drawing out features of interest in social media. The Scalable Reasoning System is an application that couples a back-end server equipped with analysis algorithms and an intuitive visual in- terface to allow for investigation. We provide a componentized system that can be rapidly adapted to user needs. The in- formation in which they are most interested is featured prominently in the application. As an example, we have developed a weather and traffic monitoring application for use by emergency operators in the city of Seattle.

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Yadav, Madan Lal, Anurag Dugar y Kuldeep Baishya. "Decoding Customer Opinion for Products or Brands Using Social Media Analytics". International Journal of Intelligent Information Technologies 18, n.º 2 (abril de 2022): 1–20. http://dx.doi.org/10.4018/ijiit.296271.

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This study uses aspect level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site twitter from where a total of 4961 tweets about ten Patanjali branded products have been extracted and analysed. Along with the aspect level sentiment analysis, an opinion tagged corpora has also been developed. Machine learning approaches - Support Vector Machine (SVM), Decision Tree, and Naïve Bayes have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. Authors first identify customer preferences and / or opinions about a product or brand by analyisng online customer reviews as they express them on social media platform, twitter by using aspect level sentiment analysis. Authors also address the limitations of scarcity of opinion tagged data, required to train supervised classifiers to perform sentiment analysis by developing tagged corpora.

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Sugumaran, Poonkuzhali y Anu Barathi Bhagavathi Kannu Uma. "Real-time twitter data analytics of mental illness in COVID-19: sentiment analysis using deep neural network". Indonesian Journal of Electrical Engineering and Computer Science 26, n.º 1 (1 de abril de 2022): 560. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp560-567.

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The World Health Organization (WHO) <span>states that the COVID-19 epidemic is being treated as a pandemic, with thousands of individuals infected and dead worldwide. School and college students are suffering from their online classes without any physical activities. Working men and women are also suffering from their working situations, as lots of people have lost their jobs and unemployment rates have become high due to the pandemic, and people are also losing physical contact with other family members, friends, and colleagues. The main objective of the proposed model is to monitor and analyse the real-time Twitter data-related tweets, such as coronavirus mental illness that are commonly used while referencing the pandemic. We have compared three deep learning approaches to sentiment analysis and found them to be useful. The first deep learning technique is to use a basic recurrent neural network (RNN), and the second is to use a deep learning RRN with long short-term memory (LSTM), followed by a gated recurrent unit (GRU). The experiment results indicate that the recurrent neural network built using GRU has the maximum accuracy of 99.47% for positive, negative, and neutral words and statements in Twitter data.</span>

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Mahendra, Rahmad, Hadi Syah Putra, Douglas Raevan Faisal y Fadzil Rizki. "Gender Prediction of Indonesian Twitter Users Using Tweet and Profile Features". Jurnal Ilmu Komputer dan Informasi 15, n.º 2 (2 de julio de 2022): 131–41. http://dx.doi.org/10.21609/jiki.v15i2.1079.

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The increasing use of social media generates huge amounts of data which in turn triggers research into social media analytics. Social media contents can be analyzed to explore public opinion on an issue or provide the insights reflecting proxy indicators towards real-world events. Understanding the demographics of social media users can increase the potential for applications of sentiment analysis, topic modeling, and other analytical tasks. To map demographics, we need to know the latent attributes of users, such as age, gender, occupation and location of residence. Since this attribute is not directly available, we need to do some inference from the social media data. This study aims to predict the gender attribute given a Twitter user account. We conducted experiments with several supervised classifiers with feature extraction, including the use of word embedding representations. The results of this study indicate that the combination of features extracted from Tweet contents and user profile structured data can predict the gender of Twitter users in Indonesia with accuracy above 80%.

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Singh, Ravindra Kumar y Harsh Kumar Verma. "User Activity Classification and Domain-Wise Ranking Through Social Interactions". International Journal of System Dynamics Applications 11, n.º 2 (abril de 2022): 1–15. http://dx.doi.org/10.4018/ijsda.20220701.oa5.

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Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

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Cornelius, Judith, Anna Kennedy y Ryan Wesslen. "An Examination of Twitter Data to Identify Risky Sexual Practices Among Youth and Young Adults in Botswana". International Journal of Environmental Research and Public Health 16, n.º 4 (23 de febrero de 2019): 656. http://dx.doi.org/10.3390/ijerph16040656.

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Botswana has the third highest rate of HIV infection, as well as one of the highest mobile phone density rates in the world. The rate of mobile cell phone adoption has increased three-fold over the past 10 years. Due to HIV infection rates, youth and young adults are the primary target for prevention efforts. One way to improve prevention efforts is to examine how risk reduction messages are disseminated on social media platforms such as Twitter. Thus, to identify key words related to safer sex practices and HIV prevention, we examined three months of Twitter data in Botswana. 1 December 2015, was our kick off date, and we ended data collection on 29 February 2016. To gather the tweets, we searched for HIV-related terms in English and in Setswana. From the 140,240 tweets collected from 251 unique users, 576 contained HIV-related terms. A representative sample of 25 active Twitter users comprised individuals, one government site and 2 organizations. Data revealed that tweets related to HIV prevention and AIDS did not occur more frequently during the month of December when compared to January and February (t = 3.62, p > 0.05). There was no significant difference between the numbers of HIV related tweets that occurred from 1 December 2015 to 29 February 2016 (F = 32.1, p > 0.05). The tweets occurred primarily during the morning and evening hours and on Tuesdays followed by Thursdays and Fridays. The least number of tweets occurred on Sunday. The highest number of followers was associated with the Botswana government Twitter site. Twitter analytics was found to be useful in providing insight into information being tweeted regarding risky sexual behaviors.

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Himawan, Arif, Muhammad Rifqi Maarif y Ulfi Saidata Aesyi. "Analisis Hashtag pada Twitter untuk Eksplorasi Pokok Bahasan Terkini Mengenai Business Intelligence". JISKA (Jurnal Informatika Sunan Kalijaga) 6, n.º 2 (3 de mayo de 2021): 106–12. http://dx.doi.org/10.14421/jiska.2021.6.2.106-112.

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The main purpose of this paper is to examine the dominant topics about Business Intelligence in micro-blogging Twitter. There are 7.153 tweets collected from Twitter API. Text mining and natural language processing are used to analyze the dominant topics among those tweets. Computational method used to count the most frequent hashtag that appears together with Business Intelligence hashtag. Twitter users are large and scattered around the world with a diverse range of skills (expertise) that can give a new perspective on a subject that may not be predicted before. For example, for topics related to Business Intelligence, the very dominant general topic discussed in the scientific literature are about data management, as well as for analytics and machine learning data. The result contributes to understanding dominant topics about Business Intelligence that can help researchers to level their research.

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