Статті в журналах: "TWITTER ANALYTICS"

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Goonetilleke, Oshini, Timos Sellis, Xiuzhen Zhang, and Saket Sathe. "Twitter analytics." ACM SIGKDD Explorations Newsletter 16, no. 1 (September 25, 2014): 11–20. http://dx.doi.org/10.1145/2674026.2674029.

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Hoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh, and Radhika Gopi. "Visual Twitter Analytics (Vista)." Online Information Review 40, no. 1 (February 8, 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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Gukanesh, A. V., G. Karthick Kumar, and 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 (April 30, 2018): 1600–1603. http://dx.doi.org/10.31142/ijtsrd11457.

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Zhang, Min, Feng-Ru Sheu, and Yin Zhang. "Understanding Twitter use by major LIS professional organisations in the United States." Journal of Information Science 44, no. 2 (January 27, 2017): 165–83. http://dx.doi.org/10.1177/0165551516687701.

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Although Twitter has been widely adopted by professional organisations, there has been a lack of understanding and research on its utilisation. This article presents a study that looks into how five major library and information science (LIS) professional organisations in the United States use Twitter, including the American Library Association (ALA), Special Libraries Association (SLA), Association for Library and Information Science Education (ALISE), Association for Information Science and Technology (ASIS&T) and the iSchools. Specifically explored are the characteristics of Twitter usage, such as prevalent topics or contents, type of users involved, as well as the user influence based on number of mentions and retweets. The article also presents the network interactions among the LIS associations on Twitter. A systematic Twitter analysis framework of descriptive analytics, content analytics, user analysis and network analytics with relevant metrics used in this study can be applied to other studies of Twitter use.

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Kota, Venkata Krishna, Venkateswarlu Naik B, and Vasudeva Rao Prasadula. "Smart City Service Monitoring Using Twitter Analytics." International Journal of Scientific and Research Publications (IJSRP) 9, no. 8 (August 24, 2019): p92136. http://dx.doi.org/10.29322/ijsrp.9.08.2019.p92136.

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Razis, Gerasimos, Georgios Theofilou, and Ioannis Anagnostopoulos. "Latent Twitter Image Information for Social Analytics." Information 12, no. 2 (January 21, 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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Alathur, Sreejith, and Rajesh Pai. "Social Media Games: Insights from Twitter Analytics." International Journal of Web Based Communities 16, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijwbc.2020.10026216.

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Pai, Rajesh R., and Sreejith Alathur. "Social media games: insights from Twitter analytics." International Journal of Web Based Communities 16, no. 1 (2020): 34. http://dx.doi.org/10.1504/ijwbc.2020.105127.

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Pai, Rajesh R., and Sreejith Alathur. "Assessing mobile health applications with twitter analytics." International Journal of Medical Informatics 113 (May 2018): 72–84. http://dx.doi.org/10.1016/j.ijmedinf.2018.02.016.

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Drescher, Larissa S., Carola Grebitus, and Jutta Roosen. "Exploring Food Consumption Trends on Twitter with Social Media Analytics: The Example of #Veganuary." EuroChoices 22, no. 2 (August 2023): 45–52. http://dx.doi.org/10.1111/1746-692x.12403.

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SummaryUsing the example of the hashtag #veganuary, a neologism of vegan and January, on Twitter with over 52,000 tweets from 2022, this article shows how Social Media Analytics can provide valuable insights into timing, volume and sentiment within any emerging (consumer) trend. Social Media Analytics is increasingly being used for the analysis of Social Media data. Whether consumers, politicians or entrepreneurs, all stakeholders in the food value chain are present on Social Media and talk about various trends in food and agriculture. In the form of an overview article, this contribution uses the example of the Vegan Challenge to demonstrate how a combination of the manifold methods of Social Media Analytics can provide extensive insights into the public discourse on food topics. It shows that #veganuary communication on Twitter has a predominantly positive connotation in the discussion of all stakeholders involved. The Vegan Challenge can also be categorised as a strong marketing campaign with a competitive character. #veganuary is commonly discussed on Twitter in tweets related to topics, such as veganism and the climate crisis. We argue that Social Media Analytics usefully extends classical analytical tools of consumer research on emerging and spreading food trends, and offers opportunities for many research studies.

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Negara, Edi Surya, Ria Andryani, and Prihambodo Hendro Saksono. "Analisis Data Twitter: Ekstraksi dan Analisis Data G eospasial." Jurnal INKOM 10, no. 1 (November 21, 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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Oliver, Esther, María Carmen Llasat, Montserrat Llasat-Botija, and Javier Díez-Palomar. "Twitter’s Messages about Hydrometeorological Events. A Study on the Social Impact of Climate Change." Sustainability 13, no. 6 (March 23, 2021): 3579. http://dx.doi.org/10.3390/su13063579.

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This study is based on an interdisciplinary collaboration between scientists from natural and social sciences to create scientific knowledge about how Twitter is valuable to understand the social impact of hydrometeorological events. The capacity of citizens’ reaction through Twitter to environmental issues is widely analyzed in the current scientific literature. Previous scientific works, for example, investigated the role of social media in preventing natural disasters. This study gives scientific evidence on the existence of diversity in the intentionality of Twitters’ messages related to hydrometeorological events. The methodological design is formed by four experiments implemented in different moments of a temporal axis. The social impact on social media methodology (SISM) is implemented as social media analytics. From the findings obtained, it can be observed that there are different forms of intentionality in Twitter’s messages related to hydrometeorological events depending on the contextual circumstances and on the characteristics of Twitter’s users’ profiles (including the geolocation when this information is available). This content is relevant for future works addressed to define social media communication strategies that can promote specific reactions in vulnerable groups in front the climate change.

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Fischman, David L., Mamas A. Mamas, Mirvat Alasnag, Purvi Parwani, Michael P. Savage, and Tejas Desai. "Understanding the Analytics of Twitter in Cardiovascular Medicine." JACC: Case Reports 2, no. 5 (May 2020): 837–39. http://dx.doi.org/10.1016/j.jaccas.2020.03.008.

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Singh, Ravindra Kumar. "Effective Information Retrieval Framework for Twitter Data Analytics." International Journal of Information Retrieval Research 13, no. 1 (July 14, 2023): 1–21. http://dx.doi.org/10.4018/ijirr.325798.

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The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real time processing of social media data to capture the insights about user's sentiment polarity, user's opinions, and current trends. In recent years, lots of studies were conducted around the processing of data to achieve higher accuracy. But reducing the time of processing still remained challenging. Later, big data technologies came into existence to solve these challenges but those have its own set of complexities along with having hardware deadweight on the system. The contribution of this article is to touch upon mentioned challenges by presenting a climbable, quick and fault tolerant framework to process real-time data to extract hidden insights. This framework is versatile enough to support batch processing along with real time data streams in parallel and distributed environment. Experimental analysis of proposed framework on twitter posts concludes it as quicker, robust, fault tolerant, and comparatively more accurate with traditional approaches.

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Haghighati, Amir, and Kamran Sedig. "VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data." Data 5, no. 1 (February 19, 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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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 (July 2015): 247–59. http://dx.doi.org/10.1016/j.ijpe.2014.12.037.

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Rodrigues, Anisha P., Roshan Fernandes, Adarsh Bhandary, Asha C. Shenoy, Ashwanth Shetty, and M. Anisha. "Real-Time Twitter Trend Analysis Using Big Data Analytics and Machine Learning Techniques." Wireless Communications and Mobile Computing 2021 (October 25, 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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Et. al., Vedant Karmalkar,. "Twego Trending: Data Analytics Based Search Engine Using Elasticsearch." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 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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Udanor, Collins, Stephen Aneke, and Blessing Ogechi Ogbuokiri. "Determining social media impact on the politics of developing countries using social network analytics." Program 50, no. 4 (September 6, 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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Al-Ibrahim, Fatimah, and Zakarya A. Alzamil. "Big Data Contextual Analytics Study on Arabic Tweets Summarization." International Journal of Knowledge and Systems Science 10, no. 4 (October 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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Harfoushi, Osama, Dana Hasan, and Ruba Obiedat. "Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison." Modern Applied Science 12, no. 7 (June 21, 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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Singh, Shiwangi, Akshay Chauhan, and Sanjay Dhir. "Analyzing the startup ecosystem of India: a Twitter analytics perspective." Journal of Advances in Management Research 17, no. 2 (November 18, 2019): 262–81. http://dx.doi.org/10.1108/jamr-08-2019-0164.

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Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

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Sholehurrohman, Ridho, and Igit Sabda Ilman. "ANALISIS SENTIMEN TWEET KASUS KEBOCORAN DATA PENGGUNAAN FACEBOOK OLEH CAMBRIGDE ANALYTICA." Jurnal Pepadun 3, no. 1 (April 1, 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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Firdaniza, Firdaniza, Budi Nurani Ruchjana, Diah Chaerani, and Jaziar Radianti. "Information Diffusion Model in Twitter: A Systematic Literature Review." Information 13, no. 1 (December 28, 2021): 13. http://dx.doi.org/10.3390/info13010013.

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Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion–exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion.

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Lee, George, Jimmy Lin, Chuang Liu, Andrew Lorek, and Dmitriy Ryaboy. "The unified logging infrastructure for data analytics at Twitter." Proceedings of the VLDB Endowment 5, no. 12 (August 2012): 1771–80. http://dx.doi.org/10.14778/2367502.2367516.

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Ho, Shuyuan Mary, Dayu Kao, Ming-Jung Chiu-Huang, Wenyi Li, and Chung-Jui Lai. "Detecting Cyberbullying “Hotspots” on Twitter: A Predictive Analytics Approach." Forensic Science International: Digital Investigation 32 (April 2020): 300906. http://dx.doi.org/10.1016/j.fsidi.2020.300906.

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Song, Jonghyuk, Sangho Lee, and Jong Kim. "Inference Attack on Browsing History of Twitter Users Using Public Click Analytics and Twitter Metadata." IEEE Transactions on Dependable and Secure Computing 13, no. 3 (May 1, 2016): 340–54. http://dx.doi.org/10.1109/tdsc.2014.2382577.

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Setianto, Yearry Panji. "Melihat Perbincangan #Pilpres2019 di Media Sosial dengan Social Media Analytics." Ultimacomm: Jurnal Ilmu Komunikasi 12, no. 1 (June 2, 2020): 14–33. http://dx.doi.org/10.31937/ultimacomm.v12i1.1088.

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Riset deskriptif ini berupaya menjelaskan tentang partisipasi warganet dalam membicarakan beragam topik politik seputar Pemilihan Umum 2019 di Indonesia. Dengan mengambil kasus diskusi online di platform media sosial Twitter, peneliti menganalisis sejumlah hashtag terkait dengan kampanye online dari kedua pasangan calon presiden dan wakil presiden, Joko Widodo-Ma’ruf Amin dan Prabowo Subianto-Sandiaga Uno. Menggunakan metode penelitian social network analytics, peneliti menemukan bahwa diskusi online di Twitter dalam sejumlah topik seperti #DebatPilpres2019, #PrabowoMenangDebat dan #DebatPintarJokowi seringkali didominasi oleh akun-akun yang tidak selalu popular serta tidak selalu dilakukan oleh ‘buzzer’ politik; meskipun akun @jokowi dan @prabowo masih merupakan dua tokoh utama yang diperbincangkan dalam diskusi tersebut. Dalam konteks ini, peneliti melihat bahwa media sosial semacam Twitter dapat berperan sebagai mini-publics, di mana diskursus yang hadir tidak harus selalu sejalan dengan wacana dominan (di media massa maupun di masyarakat luas). Selain itu, partisipasi masyarakat dalam berbincang soal politik juga dapat dimaknai sebagai praktik online politics yang tidak kalah pentingnya dalam proses demokratisasi di Indonesia. Kata Kunci: komunikasi politik, hashtag politics, social media analytics, online politics

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Maldonado, Miguel, and Vicenta Sierra. "Twitter Predicting the 2012 US Presidential Election?" Journal of Organizational and End User Computing 28, no. 3 (July 2016): 10–30. http://dx.doi.org/10.4018/joeuc.2016070102.

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Throughout the history of elections, political marketing services have led significant efforts aimed at predicting electoral outcomes as essential evidence to refine campaign tactics. This study develops an analytical procedure based on the Wisdom of Crowds effect and on a supervised approach of text analytics over social media content to predict electoral outcomes. Direct application of this procedure is illustrated analyzing 508,000 tweets about the 2012 US presidential election, obtaining results that consistently predicted President Barack Obama as the victor from seven weeks before the election. The study outperformed several traditional polls and similar studies employing social media to estimate potential election outcomes. This procedure offers an efficient alternative to political marketing services and political campaign staff practitioners interested in developing electoral predictions. Contributions to the field, procedural limitations, additional opportunities for knowledge creation, and research streams derived are introduced.

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Grasso, Valentina, Alfonso Crisci, Marco Morabito, Paolo Nesi, Gianni Pantaleo, Imad Zaza, and Bernardo Gozzini. "Italian codified hashtags for weather warning on Twitter – who is really using them?" Advances in Science and Research 14 (April 4, 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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Balan, Shilpa, and Janhavi Rege. "Mining for Social Media: Usage Patterns of Small Businesses." Business Systems Research Journal 8, no. 1 (March 28, 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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Park, Seunghyun Brian, Jichul Jang, and Chihyung Michael Ok. "Analyzing Twitter to explore perceptions of Asian restaurants." Journal of Hospitality and Tourism Technology 7, no. 4 (November 14, 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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Mnif, Emna, Khaireddine Mouakhar, and Anis Jarboui. "Blockchain technology awareness on social media: Insights from twitter analytics." Journal of High Technology Management Research 32, no. 2 (November 2021): 100416. http://dx.doi.org/10.1016/j.hitech.2021.100416.

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Nelson, Jonathan, Sterling Quinn, Brian Swedberg, Wanghuan Chu, and Alan MacEachren. "Geovisual Analytics Approach to Exploring Public Political Discourse on Twitter." ISPRS International Journal of Geo-Information 4, no. 1 (March 5, 2015): 337–66. http://dx.doi.org/10.3390/ijgi4010337.

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Singh, Prabhsimran, Karanjeet Singh Kahlon, Ravinder Singh Sawhney, Rajan Vohra, and Sukhmanjit Kaur. "Social media buzz created by #nanotechnology: insights from Twitter analytics." Nanotechnology Reviews 7, no. 6 (December 19, 2018): 521–28. http://dx.doi.org/10.1515/ntrev-2018-0053.

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AbstractThe word “nanotechnology” has been exaggerated not only by media but also by scientist groups who have overstated the unforeseen benefits of nanotechnology to validate research funding. Even ecologists, who normally remain indulged in doom-and-gloom divinations, use this word to fuel their own motives. Such outcomes lead to widespread misinformation and an unaware public. This research work is a staunch effort to filter the Twitter-based public opinions related to this word. Our results clearly indicate more of positive sentiments attached to the subject of nanotechnology, as trust, anticipation and joy overweigh by many folds the anger, mistrust and anger related to nanotechnology.

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Bhaik, Anubha, Pradeep Kumar Gupta, Mohammad Khubeb Siddiqui, and Ruben Morales-Menendez. "Impact of COVID-19 on Societal Behavior via Twitter Analytics." Revista Argentina de Ciencias del Comportamiento 14, no. 2 (August 31, 2022): 37–48. http://dx.doi.org/10.32348/1852.4206.v14.n2.30816.

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The outbreak of the COVID – 19 pandemic has caused a notable challenge to the well-being of people all around the globe. In such times, it is of foremost importance to analyze the information posted by people on social media. In this study, a Twitter-based dataset related to COVID-19 has been analyzed, and the effect of the pandemic on societal behavior has been revealed. Tweets have been hydrated and pre-processed using the NLTK toolkit to find the most frequently posted COVID- related words. This research can help identify the social response of people to the Pandemic, realizing what people are majorly concerned about and extracting knowledge about the daily trend of sentiments around the world. It has been concluded from our analysis that rather than the expected negative trend in the use of COVID-19 terms on a daily basis, more positive figurative language has been used in the posted tweets.

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Arifin, Firman, Budi Nur Iman, Budi Nur Iman, Elly Purwantini, Elly Purwantini, Mochamad Hariadi, Mochamad Hariadi, Muhammad Anshari, and Muhammad Anshari. "Data Analytics to Examine Trending Topics for Indonesian Election 2019." INOVTEK Polbeng - Seri Informatika 4, no. 2 (November 28, 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, and Vasudeva Akula. "Identifying customer knowledge on social media through data analytics." Journal of Enterprise Information Management 32, no. 1 (February 11, 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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Larasati, Aisyah, Raretha Maren, and Retno Wulandari. "Utilizing Elbow Method for Text Clustering Optimization in Analyzing Social Media Marketing Content of Indonesian e-Commerce." Jurnal Teknik Industri 23, no. 2 (December 21, 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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Kusumanchi Naga Sireesha and Padala Srinivasa Reddy. "COVID19 Sentiment Analysis using Machine Learning Classification Algorithms." September 2021 7, no. 09 (September 27, 2021): 13–18. http://dx.doi.org/10.46501/ijmtst0709003.

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis. The diverse use of social networking sites, like Twitter, speeds up the process of sharing information and having views on community events and health crises COVID-19 has been one of Twitter's trending areas. The Twitter messages created via Twitter are named Tweets. In this paper, we identify public sentiment associated with the pandemic using Coronavirus-specific Tweets and Python, along with its sentiment analysis packages. We provide an overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. This research provides insights into Coronavirus fear sentiment progression, associated methods, limitations, and different opportunities. In this project, we have designed a Sentiment analysis System that would identify the sentiment of a tweet and classify it into one of the five classes they include:”ExtremelyPositive”,“Positive”,”Neutral”, ”Negative” and “Extremely Negative”.

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Thangavel, Chandrakumar, Ramya Thangavel, Elangovan Ramanujam, Deepthi Tabitha Bennet, and Preethi Samantha Bennet. "Consumer Perception of Internet Banking and Mobile Banking Using Twitter Analytics." International Journal of Sociotechnology and Knowledge Development 14, no. 1 (January 2022): 1–14. http://dx.doi.org/10.4018/ijskd.297978.

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Mobile and Internet banking have introduced a new way of monetary transactions without the need for physical presence. This research proposes to analyze the sentiments of people regarding digital transactions, Mobile and Internet banking. The explosion of Internet usage and the huge funding initiatives in electronic banking has drawn the attention of researchers towards Internet and mobile banking. This study focuses on customer value perceptions of the Internet and mobile banking in India. The recent and forecasted Digital India scheme shows high growth in e-banking in India. The demographic, attitudinal, and behavioral characteristics of mobile bank users were examined. In this study, datasets obtained from Twitter were used. After extensive and repeated analysis, it is found that both Mobile and Internet banking are well received, the number of positive Tweets, especially regarding mobile banking, is much higher than that of Internet banking. This leads to the interpretation that people find mobile banking easier and safer, especially during the ongoing COVID-19 pandemic.

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Walker, Rick, Llyr ap Cenydd, Serban Pop, Helen C. Miles, Chris J. Hughes, William J. Teahan, and Jonathan C. Roberts. "Storyboarding for visual analytics." Information Visualization 14, no. 1 (May 28, 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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Lou, Shanshan. "Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study." Journal of Advertising Education 21, no. 1 (May 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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Pradana, Meirza Luthfi, Vincencius Alvian Pratama, Rika Aulia Ramdhani, and 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, no. 2 (December 30, 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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Yadav, Madan Lal, Anurag Dugar, and Kuldeep Baishya. "Decoding Customer Opinion for Products or Brands Using Social Media Analytics." International Journal of Intelligent Information Technologies 18, no. 2 (April 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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Shankar, Shardul, and Vijayshri Tewari. "Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter." Journal of Intelligence 9, no. 4 (November 24, 2021): 56. http://dx.doi.org/10.3390/jintelligence9040056.

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Social networks have created an information diffusion corpus that provides users with an environment where they can express their views, form a community, and discuss topics of similar or dissimilar interests. Even though there has been an increasingly rising demand for conducting an emotional analysis of the users on social media platforms, the field of emotional intelligence (EI) has been rather slow in exploiting the enormous potential that social media can play in the research and practice of the framework. This study, thus, tried to examine the role that the microblogging platform Twitter plays in enhancing the understanding of the EI community by building on the Twitter Analytics framework of Natural Language Processing to further develop the insights of EI research and practice. An analysis was conducted on 53,361 tweets extracted using the hashtag emotional intelligence through descriptive analytics (DA), content analytics (CA), and network analytics (NA). The findings indicated that emotional intelligence tweets are used mostly by speakers, psychologists (or other medical professionals), and business organizations, among others. They use it for information dissemination, communication with stakeholders, and hiring. These tweets carry strong positive sentiments and sparse connectedness. The findings present insights into the use of social media for understanding emotional intelligence.

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Park, Hyejin, Ivan Ureta, and Boyoung Kim. "Trend Analysis of Decentralized Autonomous Organization Using Big Data Analytics." Information 14, no. 6 (June 9, 2023): 326. http://dx.doi.org/10.3390/info14060326.

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Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were from the identification of the top 100 frequently appearing keywords, as well as the top 20 keywords with high network centrality, and key topics related to finance, gaming, and fundraising, from both Twitter and Reddit. The analysis revealed twelve topics from Twitter and eight topics from Reddit, with the term “community” frequently appearing across many of these topics. The findings provide valuable insights into the current trend and future potential of DAOs, and should be used by researchers to guide further research in the field and by decision makers to explore innovative ways to govern the organizations.

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Luo, Jianhong, Jingcheng Du, Cui Tao, Hua Xu, and Yaoyun Zhang. "Exploring temporal suicidal behavior patterns on social media: Insight from Twitter analytics." Health Informatics Journal 26, no. 2 (March 14, 2019): 738–52. http://dx.doi.org/10.1177/1460458219832043.

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A valid mechanism for suicide detection and intervention to a wider population online has not yet been fully established. With the increasing suicide rate, we proposed an approach that aims to examine temporal patterns of potential suicidal ideations and behaviors on Twitter to better understand their risk factors and time-varying features. It identifies latent suicide topics and then models the suicidal topic–related score time series to quantitatively represent behavior patterns on Twitter. After evaluated on a collection of suicide-related tweets in 2016, 13 key risk factors were discovered and the temporal patterns of suicide behavior on different days during 1 week were identified to highlight the distinct time-varying features related to different risk factors. This study is practical to help public health services and others to develop refined prevention strategies, to monitor and support a population of high-risk at right moments.

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Lynn, Theo, Pierangelo Rosati, and Binesh Nair. "Calculated vs. Ad Hoc Publics in the #Brexit Discourse on Twitter and the Role of Business Actors." Information 11, no. 9 (September 10, 2020): 435. http://dx.doi.org/10.3390/info11090435.

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Mobilization theory posits that social media gives a voice to non-traditional actors in socio-political discourse. This study uses network analytics to understand the underlying structure of the Brexit discourse and whether the main sub-networks identify new publics and influencers in political participation, and specifically industry stakeholders. Content analytics and peak detection analysis are used to provide greater explanatory values to the organizing themes for these sub-networks. Our findings suggest that the Brexit discourse on Twitter can be largely explained by calculated publics organized around the two campaigns and political parties. Ad hoc communities were identified based on (i) the media, (ii) geo-location, and (iii) the US presidential election. Other than the media, significant sub-communities did not form around industry as whole or around individual sectors or leaders. Participation by business accounts in the Twitter discourse had limited impact.

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Wang, Yinying, and David J. Fikis. "Common Core State Standards on Twitter: Public Sentiment and Opinion Leaders." Educational Policy 33, no. 4 (August 2, 2017): 650–83. http://dx.doi.org/10.1177/0895904817723739.

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The purpose of this study is to examine the public opinion on the Common Core State Standards (CCSS) on Twitter. Using Twitter Application Program Interface (API), we collected the tweets containing the hashtags #CommonCore and #CCSS for 12 months from 2014 to 2015. A Common Core corpus was created by compiling all the collected 660,051 tweets. The results of sentiment analysis suggest Twitter users expressed overwhelmingly negative sentiment toward the CCSS in all 50 states. Five topic clusters were detected by cluster analysis of the hashtag co-occurrence network. We also found that most of the opinion leaders were those who expressed negative sentiment toward the CCSS on Twitter. This study for the first time demonstrates how text mining techniques can be applied to education policy research, laying the foundation for real-time analytics of public opinion on education policies, thereby informing policymaking and implementation.

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