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Journal articles on the topic 'Tweets networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Mitchell, Brett G., Philip L. Russo, Jonathan A. Otter, Martin A. Kiernan, and Landon Aveling. "What Makes a Tweet Fly? Analysis of Twitter Messaging at Four Infection Control Conferences." Infection Control & Hospital Epidemiology 38, no. 11 (2017): 1271–76. http://dx.doi.org/10.1017/ice.2017.170.

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OBJECTIVETo examine tweeting activity, networks, and common topics mentioned on Twitter at 4 international infection control and infectious disease conferences.DESIGNA cross-sectional study.METHODSAn independent company was commissioned to undertake a Twitter ‘trawl’ each month between July 1, 2016, and November 31, 2016. The trawl identified any tweets that contained the official hashtags of the conferences for (1) the UK Infection Prevention Society, (2) IDWeek 2016, (3) the Federation of Infectious Society/Hospital Infection Society, and (4) the Australasian College for Infection Prevention

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Al-Saggaf, Yeslam, Sonja Utz, and Ruoyun Lin. "Venting Negative Emotions on Twitter and The Number of Followers and Followees." International Journal of Sociotechnology and Knowledge Development 8, no. 1 (2016): 44–55. http://dx.doi.org/10.4018/ijskd.2016010103.

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Do people who express negative feelings (loneliness, sadness) on Twitter gain or lose online contacts? To answer this question, the authors tracked the number of followers and followees of people who tweeted about loneliness or sadness twice; once when they expressed the negative feeling and a second time five months later. The authors compared the networks of those users with the networks of others who either simply retweeted tweets about loneliness/sadness or (re)tweeted about the corresponding positive feelings. People expressing loneliness in their tweets, as well as people expressing sadn

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Haunschild, Robin, Lutz Bornmann, Devendra Potnis, and Iman Tahamtan. "Investigating dissemination of scientific information on Twitter: A study of topic networks in opioid publications." Quantitative Science Studies 2, no. 4 (2021): 1486–510. http://dx.doi.org/10.1162/qss_a_00168.

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Abstract While previous research has mostly focused on the “number of mentions” of scientific research on social media, the current study applies “topic networks” to measure public attention to scientific research on Twitter. Topic networks are the networks of co-occurring author keywords in scholarly publications and networks of co-occurring hashtags in the tweets mentioning those publications. We investigate which topics in opioid scholarly publications have received public attention on Twitter. Additionally, we investigate whether the topic networks generated from the publications tweeted b

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Sterner, Glenn, and Diane Felmlee. "The Social Networks of Cyberbullying on Twitter." International Journal of Technoethics 8, no. 2 (2017): 1–15. http://dx.doi.org/10.4018/ijt.2017070101.

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This research applies a social network perspective to the issue of cyber aggression, or cyberbullying, on the social media platform Twitter. Cyber aggression is particularly problematic because of its potential for anonymity, and the ease with which so many others can join the harassment of victims. Utilizing a comparative case study methodology, the authors examined thousands of Tweets to explore the use of denigrating slurs and insults contained in public tweets that target an individual's gender, race, or sexual orientation. Findings indicate cyber aggression on Twitter to be extensive and

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Sarkar, Kamal. "Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks." Journal of Intelligent Systems 28, no. 3 (2019): 377–86. http://dx.doi.org/10.1515/jisys-2017-0418.

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Abstract Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks

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Tsugawa, Sho. "Empirical Analysis of the Relation between Community Structure and Cascading Retweet Diffusion." Proceedings of the International AAAI Conference on Web and Social Media 13 (July 6, 2019): 493–504. http://dx.doi.org/10.1609/icwsm.v13i01.3247.

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Social networks have community structure, in which the network is composed of highly clustered subnetworks (communities) with sparse links between them. Such community structure is expected to affect information diffusion among individuals. This paper empirically investigates how the community structure of a social network among Twitter users affects cascading diffusion of retweets among them. The results show that the frequency of retweets between users who are in the same community is approximately two times that between users who are in different communities. In contrast, the results also s

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Torres-Lugo, Christopher, Manita Pote, Alexander C. Nwala, and Filippo Menczer. "Manipulating Twitter through Deletions." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 1029–39. http://dx.doi.org/10.1609/icwsm.v16i1.19355.

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Research into influence campaigns on Twitter has mostly relied on identifying malicious activities from tweets obtained via public APIs. By design, these approaches ignore deleted tweets. However, bad actors can delete content strategically to manipulate the system. Here, we provide the first exhaustive, large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million accounts. Estimates based on publicly available Twitter data underestimate the true deletion volume. A small fraction of accounts delete a large number of tweets daily. We uncover two

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Magelinski, Thomas, and Kathleen M. Carley. "Contextualizing Online Conversational Networks." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 590–601. http://dx.doi.org/10.1609/icwsm.v17i1.22171.

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Online social connections occur within a specific conversational context. Prior work in network analysis of social media data attempts to contextualize data through filtering. We propose a method of contextualizing online conversational connections automatically and illustrate this method with Twitter data. Specifically, we detail a graph neural network model capable of representing tweets in a vector space based on their text, hashtags, URLs, and neighboring tweets. Once tweets are represented, clusters of tweets uncover conversational contexts. We apply our method to a dataset with 4.5 milli

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Brassel, Sophie, Melissa Brunner, Andrew Campbell, Emma Power, and Leanne Togher. "Exploring Discussions About Virtual Reality on Twitter to Inform Brain Injury Rehabilitation: Content and Network Analysis." Journal of Medical Internet Research 26 (January 19, 2024): e45168. http://dx.doi.org/10.2196/45168.

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Background Virtual reality (VR) use in brain injury rehabilitation is emerging. Recommendations for VR development in this field encourage end user engagement to determine the benefits and challenges of VR use; however, existing literature on this topic is limited. Data from social networking sites such as Twitter may further inform development and clinical practice related to the use of VR in brain injury rehabilitation. Objective This study collected and analyzed VR-related tweets to (1) explore the VR tweeting community to determine topics of conversation and network connections, (2) unders

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Rahmat, Al Fauzi, and M. Rafi. "Social Media Network Analysis on Twitter Users Network to the Pension Plan Policy." Communicare : Journal of Communication Studies 8, no. 1 (2022): 62. http://dx.doi.org/10.37535/101009120225.

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This article scrutinizes the network of Twitter users on the dissemination of tweets on the Old-Age Guarantee policy in Indonesia. A qualitative method with social media network analysis approach was used. Then, data sources were obtained from Twitter social media through #JHT_JokowiHarusTurun, #jaminanharitua, and #JHT. Furthermore, to manage data source, NVivo 12 plus software was used to analyze qualitative data from Twitter social media – including dissemination rate of tweets, followed by geographical map tweet stream, Twitter user’ network pattern, sentiment proportion, as well as words

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Hashemi, Mahdi. "A Data-Driven Framework for Coding the Intent and Extent of Political Tweeting, Disinformation, and Extremism." Information 12, no. 4 (2021): 148. http://dx.doi.org/10.3390/info12040148.

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Disinformation campaigns on online social networks (OSNs) in recent years have underscored democracy’s vulnerability to such operations and the importance of identifying such operations and dissecting their methods, intents, and source. This paper is another milestone in a line of research on political disinformation, propaganda, and extremism on OSNs. A total of 40,000 original Tweets (not re-Tweets or Replies) related to the U.S. 2020 presidential election are collected. The intent, focus, and political affiliation of these political Tweets are determined through multiple discussions and rev

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Kedar, S. V. "Stock Market Increase and Decrease using Twitter Sentiment Analysis and ARIMA Model." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 146–61. http://dx.doi.org/10.17762/turcomat.v12i1s.1596.

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With centuries and decades, people started evolving and slowly started entering into technology era. Social networks era came before everyone which connected people from far away countries. Such an example of social network are applications like Twitter, Facebook, Instagram, LinkedIn etc. Every application has its own significance. Such an application is Twitter where people tweet regarding their opinion about a topic, a person anything. The tweets regarding company its performance and people’s opinion about the stock is also tweeted. People like to invest in stocks using this data posted of s

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Kiss, István Márton, and Norbert Buzás. "Who Tweets About Technology?" International Journal of Knowledge and Systems Science 6, no. 1 (2015): 46–59. http://dx.doi.org/10.4018/ijkss.2015010104.

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Due to the growing importance and influence of social media, it is timely to gain knowledge about the communication related to technological innovations in the cyberspace. The main goal of the present article is to shed light on the process and structure of information sharing about technologies on Twitter. It identifies the most relevant clusters of actors in the communication, and investigates the network structures existing among them in the case of two novel operation systems. The authors found disparate network properties from other topics, unexpectedly robust networks, unbalanced activit

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Shahin, Saif, and Zehui Dai. "Understanding Public Engagement With Global Aid Agencies on Twitter: A Technosocial Framework." American Behavioral Scientist 63, no. 12 (2019): 1684–707. http://dx.doi.org/10.1177/0002764219835248.

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This study develops a technosocial framework for assessing the efficacy of global aid agencies’ use of Twitter’s algorithmic affordances for participatory social change. We combine computational and interpretive methods to examine tweets posted by three global aid agencies—U.S. Agency for International Development, Swedish International Development Cooperation Agency, and the International Committee of the Red Cross—as well as public tweets that mention these agencies ( N = ~100,000). Results indicate that when an agency (a) replies to or retweets public tweeters, (b) includes publicly oriente

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Song, Guizhe, and Degen Huang. "A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data." Future Internet 13, no. 7 (2021): 163. http://dx.doi.org/10.3390/fi13070163.

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The massive amount of data generated by social media present a unique opportunity for disaster analysis. As a leading social platform, Twitter generates over 500 million Tweets each day. Due to its real-time characteristic, more agencies employ Twitter to track disaster events to make a speedy rescue plan. However, it is challenging to build an accurate predictive model to identify disaster Tweets, which may lack sufficient context due to the length limit. In addition, disaster Tweets and regular ones can be hard to distinguish because of word ambiguity. In this paper, we propose a sentiment-a

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Al-Tarawneh, Ahmed, and Ja’afer Al-Saraireh. "Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 12321–37. http://dx.doi.org/10.3233/jifs-210458.

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Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks techni

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Passaro, Antonio, Graham Mackenzie, Matteo Lambertini, et al. "European Society for Medical Oncology (ESMO) 2018 Congress Twitter analysis: from ethics to results through the understanding of communication and interaction flows." ESMO Open 5, no. 1 (2020): e000598. http://dx.doi.org/10.1136/esmoopen-2019-000598.

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BackgroundTwitter is a microblogging service providing a platform for social networking. For medical information, Twitter is an interesting channel for sharing and spreading information and as an engagement platform for different stakeholders. Benefits and caveats of uncontrolled medical information must be carefully pondered, considering the possible intended and unintended adverse outcomes of uncontrolled influencing. The aim of this study was to describe the non-commercial content shared on Twitter and to analyse the level of influence of commercial tweeters during the European Society of M

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Helmstetter, Stefan, and Heiko Paulheim. "Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision." Future Internet 13, no. 5 (2021): 114. http://dx.doi.org/10.3390/fi13050114.

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The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor, and recent approaches utilizing distributional semantics require large training corpora. In this paper, we introduce an alternative approach for creating a large-scale dataset for tweet classification

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Turner, Jason, Mehmed Kantardzic, and Rachel Vickers-Smith. "Infodemiological Examination of Personal and Commercial Tweets About Cannabidiol: Term and Sentiment Analysis." Journal of Medical Internet Research 23, no. 12 (2021): e27307. http://dx.doi.org/10.2196/27307.

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Background In the absence of official clinical trial information, data from social networks can be used by public health and medical researchers to assess public claims about loosely regulated substances such as cannabidiol (CBD). For example, this can be achieved by comparing the medical conditions targeted by those selling CBD against the medical conditions patients commonly treat with CBD. Objective The objective of this study was to provide a framework for public health and medical researchers to use for identifying and analyzing the consumption and marketing of unregulated substances. Spe

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Ahsan, Mohammad, Madhu Kumari, Tajinder Singh, and Triveni Lal Pal. "Sentiment Based Information Diffusion in Online Social Networks." International Journal of Knowledge Discovery in Bioinformatics 8, no. 1 (2018): 60–74. http://dx.doi.org/10.4018/ijkdb.2018010105.

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This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e.

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Safarnejad, Lida, Qian Xu, Yaorong Ge, Siddharth Krishnan, Arunkumar Bagarvathi, and Shi Chen. "Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency." American Journal of Public Health 110, S3 (2020): S340—S347. http://dx.doi.org/10.2105/ajph.2020.305854.

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Objectives. To provide a comprehensive workflow to identify top influential health misinformation about Zika on Twitter in 2016, reconstruct information dissemination networks of retweeting, contrast mis- from real information on various metrics, and investigate how Zika misinformation proliferated on social media during the Zika epidemic. Methods. We systematically reviewed the top 5000 English-language Zika tweets, established an evidence-based definition of “misinformation,” identified misinformation tweets, and matched a comparable group of real-information tweets. We developed an algorith

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D. Vasumathi, G. Suryanarayana, V. Kakulapati,. "User-Query Processing through Dynamic Tweets Status Recommender System." Psychology and Education Journal 58, no. 1 (2021): 5600–5606. http://dx.doi.org/10.17762/pae.v58i1.2180.

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With increasing user information volume in online social networks, recommender systems have been an effective method to limit such information overload. The requirements of recommender systems specified, with widespread adoption in many internet social Twitter, Facebook, and Google online applications. In recent years, the micro-blogging in Twitter has brought greater importance to online users as a channel spreading knowledge and information. Through Twitter, users can find the relevant information on the search they perform, but understanding the past, present, and future information relevan

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Stella, Massimo, Michael S. Vitevitch, and Federico Botta. "Cognitive Networks Extract Insights on COVID-19 Vaccines from English and Italian Popular Tweets: Anticipation, Logistics, Conspiracy and Loss of Trust." Big Data and Cognitive Computing 6, no. 2 (2022): 52. http://dx.doi.org/10.3390/bdcc6020052.

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Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. This work reconstructs how popular and trending posts framed semantically and emotionally COVID-19 vaccines on Twitter. We achieve this by merging natural language processing, cognitive network science and AI-based image analysis. We focus on 4765 unique popular tweets in English or Italian about COVID-19 vaccines between December 2020 and March 2021. One popular English tweet contained in our data set was liked around 495,000 times, highlighting how popular tweets

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Samuel, Avinash, and Dilip Kumar Sharma. "A Novel Framework for Sentiment and Emoticon-Based Clustering and Indexing of Tweets." Journal of Information & Knowledge Management 17, no. 02 (2018): 1850013. http://dx.doi.org/10.1142/s0219649218500132.

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Social Networks have become an important part of people’s life as they share their day-to-day happenings, portray their opinions on various topics or find out information related to their queries. Due to the overwhelming volume of tweets generated on a daily basis, it is not possible to read all the tweets and differentiate the tweets based on the views or the attitude they portray only. The primary objective of sentiment analysis is to find out the attitude/emotion/opinion/sentiment that is present in the material provided. Commonly, the tweets can be clustered on the basis of them being posi

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Keküllüoglu, Dilara, Kami Vaniea, Maria K. Wolters, and Walid Magdy. "Twitter has a Binary Privacy Setting, are Users Aware of How It Works?" Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023): 1–18. http://dx.doi.org/10.1145/3579537.

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Twitter accounts are public by default, but Twitter gives the option to create protected accounts, where only approved followers can see their tweets. The publicly visible information changes based on the account type and the visibility of tweets also depends solely on the poster's account type which can cause unintended disclosures especially when users interact. We surveyed 336 Twitter users to understand users' awareness of account information visibility, as well as the tweet visibility when users interact. We find that our participants are aware of the visibility of their profile informati

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Karajeh, Ola, Dirar Darweesh, Omar Darwish, Noor Abu-El-Rub, Belal Alsinglawi, and Nasser Alsaedi. "A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets." Future Internet 13, no. 1 (2021): 19. http://dx.doi.org/10.3390/fi13010019.

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Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational twee

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Sugitani, Takuya, Masumi Shirakawa, Takahiro Hara, and Shojiro Nishio. "A method for detecting local events using the spatiotemporal locality of microblog posts." International Journal of Web Information Systems 11, no. 1 (2015): 2–16. http://dx.doi.org/10.1108/ijwis-04-2014-0017.

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Purpose – The purpose of this paper is to propose a method to detect local events in real time using Twitter, an online microblogging platform. The authors especially aim at detecting local events regardless of the type and scale. Design/methodology/approach – The method is based on the observation that relevant tweets (Twitter posts) are simultaneously posted from the place where a local event is happening. Specifically, the method first extracts the place where and the time when multiple tweets are posted using a hierarchical clustering technique. It next detects the co-occurrences of key te

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Moreno, Sebastián, Danilo Bórquez-Paredes, and Valentina Martínez. "Analysis of the Characteristics and Speed of Spread of the “FUNA” on Twitter." Mathematics 11, no. 7 (2023): 1749. http://dx.doi.org/10.3390/math11071749.

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The funa is a prevalent concept in Chile that aims to expose a person’s bad behavior, punish the aggressor publicly, and warn the community about it. Despite its massive use on the social networks of Chilean society, the real dissemination of funas among communities is unknown. In this paper, we extract, generate, analyze, and compare the Twitter social network’s spread of three tweets related to “funas” against three other trending topics, through the analysis of global network characteristics over time (degree distribution, clustering coefficient, hop plot, and betweenness centrality). As ob

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Abdelkoui, Feriel, and Mohamed-Khireddine Kholladi. "Extracting Criminal-Related Events from Arabic Tweets." Journal of Information Technology Research 10, no. 3 (2017): 34–47. http://dx.doi.org/10.4018/jitr.2017070103.

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Recently, Twitter as one of social networks has been considered as a rich source of spatio-temporal information and significant revenue for mining data. Event detection from tweets can help to predict more serious real-world events. Such as: criminal events, natural hazards, and the spread of epidemics. Etc. This paper deals with event-based extraction for criminal incidents from Arabic tweets. It presents a framework that supports automated extraction of spatial and temporal information from tweets. The proposed approach is based on combining various indicators, including the names of places

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Ahmad, Faraz, and S. A. M. Rizvi. "Identification of user’s credibility on twitter social networks." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 554. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp554-563.

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<p>Twitter is one of the most influential social media platforms, facilitates the spreading of information in the form of text, images, and videos. However, the credibility of posted content is still trailed by an interrogation mark. Introduction: In this paper, a model has been developed for finding the user’s credibility based on the tweets which they had posted on Twitter social networks. The model consists of machine learning algorithms that assist not only in categorizing the tweets into credibility classes but also helps in finding user’s credibility ratings on the social media pla

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Nguyen, Minh-Tien, Tri-Thanh Nguyen, Asanobu Kitamoto, and Van-Hau Nguyen. "Exploiting Social Networks as a Live Mass Media Channel During Disasters for Reactions." International Journal on Artificial Intelligence Tools 30, no. 05 (2021): 2150024. http://dx.doi.org/10.1142/s021821302150024x.

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Social networks, e.g. Twitter, have been proved to be almost real-time systems for spreading information, that provide a valuable information channel in emergencies, e.g. disasters. This paper presents a framework designed to distill actionable tweets. The framework tackles the diversity, large volume, and noise of tweets for providing users live information for quick responses. To do that, our framework first retrieves a large number of tweets to ensure the diversity. It next removes irrelevant and indirect tweets for reducing the volume, divides informative tweets into predefined classes for

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Alabid, Noralhuda N., and Zahraa Naseer. "Summarizing twitter posts regarding COVID-19 based on n-grams." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (2023): 1008. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp1008-1015.

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The COVID-19 pandemic announced by the World Health Organization has disrupted human lives at different scales, including the economy, public health, and people's emotions. Social media databases record huge accumulated information concern this pandemic. Twitter platform is considered one of the most active social media that enable users to tweet in different conversations they are concerned about. The problem arises when tweeters want to search about a specific topic. They can only sort tweets by its recency to understand conversation and not by relevancy. This makes tweeters read through the

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Yao, Wenlin, Cheng Zhang, Shiva Saravanan, Ruihong Huang, and Ali Mostafavi. "Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 532–39. http://dx.doi.org/10.1609/aaai.v34i01.5391.

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People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses

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Fani, Syiva Multi, Rukun Santoso, and Suparti Suparti. "PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING." Jurnal Gaussian 10, no. 4 (2021): 583–93. http://dx.doi.org/10.14710/j.gauss.v10i4.30409.

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Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best

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Alim, Sophia. "Twitter Profiles of Organisations Fighting Against Cyberbullying and Bullying." International Journal of Cyber Behavior, Psychology and Learning 7, no. 3 (2017): 37–56. http://dx.doi.org/10.4018/ijcbpl.2017070104.

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Cyberbullying has become more popular on social networks especially on Twitter due to the popularity of information sharing. However, there is limited research into the tweet content and influence of Twitter profiles of organisations fighting against cyberbullying and bullying. For this article, Twitter profiles of eleven organisations were selected. Tweet contents and profiles features – the number of followers, mentions, retweets, the measure of Klout, interactor ratio and the number of tweet URL clicks – associated with influence and tweet content were analysed. Content analysis of the 321

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Thakur, Nirmalya, Kesha A. Patel, Audrey Poon, Rishika Shah, Nazif Azizi, and Changhee Han. "A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023." Future Internet 15, no. 10 (2023): 346. http://dx.doi.org/10.3390/fi15100346.

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Exoskeletons have emerged as a vital technology in the last decade and a half, with diverse use cases in different domains. Even though several works related to the analysis of Tweets about emerging technologies exist, none of those works have focused on the analysis of Tweets about exoskeletons. The work of this paper aims to address this research gap by presenting multiple novel findings from a comprehensive analysis of about 150,000 Tweets about exoskeletons posted between May 2017 and May 2023. First, findings from temporal analysis of these Tweets reveal the specific months per year when

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Wang, Qiaozhi, Jaisneet Bhandal, Shu Huang, and Bo Luo. "Content-Based Classification of Sensitive Tweets." International Journal of Semantic Computing 11, no. 04 (2017): 541–62. http://dx.doi.org/10.1142/s1793351x17400220.

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Online Social Networks (OSNs), such as Facebook and Twitter, provide open platforms for users to easily share their statuses, opinions, and ideas, ranging from personal experiences/activities to breaking news. With the increasing popularity of online social networks and the explosion of blog and microblog messages, we have observed large amounts of potentially sensitive or private messages being published to OSNs inadvertently or voluntarily. The owners of these messages may become vulnerable to online stalkers or adversaries, especially considering that many online social network platforms (e

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Sharma, Sudhir Kumar, Ximi Hoque, and Pravin Chandra. "Sentiment Predictions Using Deep Belief Networks Model for Odd-Even Policy in Delhi." International Journal of Synthetic Emotions 7, no. 2 (2016): 1–22. http://dx.doi.org/10.4018/ijse.2016070101.

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This paper analyzes the odd-even policy in Delhi using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using Deep Belief Networks classification (DBN) to classify unseen tweets on the same context. This paper collects tweets on this event under six hashtags. This study explores three freely available resources / Application Programming Interface

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Weissenbacher, Davy, Abeed Sarker, Ari Klein, Karen O’Connor, Arjun Magge, and Graciela Gonzalez-Hernandez. "Deep neural networks ensemble for detecting medication mentions in tweets." Journal of the American Medical Informatics Association 26, no. 12 (2019): 1618–26. http://dx.doi.org/10.1093/jamia/ocz156.

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Abstract Objective Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step toward incorporating Twitter data in pharmacoepidemiologic research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names suffer from low recall due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Materials and Methods We present Kusuri, an Ensemble Learning classifier able to identify tweets mentioning drug products a

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Türker, İlker, and Eyüb Ekmel Sulak. "A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links." International Journal of Modern Physics B 32, no. 04 (2018): 1850029. http://dx.doi.org/10.1142/s0217979218500297.

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Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags int

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Santarossa, Sara, Ashley Rapp, Saily Sardinas, et al. "Understanding the #longCOVID and #longhaulers Conversation on Twitter: Multimethod Study." JMIR Infodemiology 2, no. 1 (2022): e31259. http://dx.doi.org/10.2196/31259.

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Background The scientific community is just beginning to uncover the potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences. Objective The aim of this study was to investigate the #longCOVID and #longhaulers conversations on Twitter by examining the combined effects of topic discussion and social network analysis for discovery on long COVID-19. Methods A multipronged approach was used to analyze

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Pasalkar, Jayashree, Rupesh Bardiya, Yashwant Chavan, and Abhishek Dere, Rushikesh Hede. "Survey on Covid19 Misinformation Classification via Twitter using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4573–75. http://dx.doi.org/10.22214/ijraset.2022.43189.

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Abstract: It has become particularly useful for Twitter and other social networks (ONS) to disseminate information. How-ever, they have become the breeder for false information, especially in connection with the 2019 pandemic of the continued coronavirus (COVID-19). The hazards posed by these COVID-19 approaches are better defined as infodemic, and scientific evidence and sentiment classification is more important than ever. The reliability of Twitter intelligence about the COVID-19 pandemics is explored in this article. Based on our results on a vast number of tweets, we suggest an ensemble-l

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Handel, Dinah, Jessica Hochman, and Doris Santoro. "Visualizing teacher tweets: Finding professional learning networks in topical networks." Proceedings of the Association for Information Science and Technology 52, no. 1 (2015): 1–3. http://dx.doi.org/10.1002/pra2.2015.1450520100138.

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Nistor, Sergiu Cosmin, Mircea Moca, Darie Moldovan, Delia Beatrice Oprean, and Răzvan Liviu Nistor. "Building a Twitter Sentiment Analysis System with Recurrent Neural Networks." Sensors 21, no. 7 (2021): 2266. http://dx.doi.org/10.3390/s21072266.

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This paper presents a sentiment analysis solution on tweets using Recurrent Neural Networks (RNNs). The method is can classifying tweets with an 80.74% accuracy rate, considering a binary task, after experimenting with 20 different design approaches. The solution integrates an attention mechanism aiming to enhance the network, with a two-way localization system: at memory cell level and at network level. We present an in-depth literature review for Twitter sentiment analysis and the building blocks that grounded the design decisions of our solution, employed as a core classification component

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Ong’ong’a, Daniel Oloo. "Digital Diplomacy in Kenya." Society & Sustainability 5, no. 2 (2023): 1–13. http://dx.doi.org/10.38157/ss.v5i2.587.

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The study examined the digital diplomacy practices of three diplomatic actors, namely the US, the UK, and China, in Kenya. To understand how these actors communicate their messages digitally, the study conducted a content analysis of their X tweets, hashtags, hyperlinks, and visuals. Additionally, the study analyzed the tweet impressions section of the posts to gain insight into individual tweet performance, engagement, followers, and following details. These components are crucial in understanding the digital diplomacy practices of the actors. The results showed that the diplomatic actors pri

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Anderson, Katie Elson. "Getting acquainted with social networks and apps: Snapchat and the rise of ephemeral communication." Library Hi Tech News 32, no. 10 (2015): 6–10. http://dx.doi.org/10.1108/lhtn-11-2015-0076.

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Purpose – The traditional library image that comes to mind upon hearing this word is of vertical files filled with newspaper clippings, photos, brochures, flyers on a popular topic or event. Design/methodology/approach – The modern image of ephemera, however, is now more nuanced and complex, as the increased creation of digital ephemera provides opportunities and challenges for libraries and archives. Findings – There now need to be considerations for dealing with digital materials and artifacts that are short-lived and temporary. These digital ephemera are increasing every day, with a presenc

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Dunklin, Macy, and Paige Jennings. "“Where’s the Outrage??”: An Analysis of #BlackLivesMatter and #BlackTransLivesMatter Twitter Counterpublics." Journalism & Mass Communication Quarterly 99, no. 3 (2022): 763–83. http://dx.doi.org/10.1177/10776990221109236.

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Twitter activism is a powerful tool for #BlackLivesMatter, but #BlackTransLivesMatter has not seen the same success. This study examined tweets collected during 1 week in May 2020 that encompassed the deaths of George Floyd, a cisgender Black male, and Tony McDade, a transgender Black male. Using a mixed-methods approach, we analyzed semantic networks and conducted a critical discourse analysis to determine how satellite publics function within activism discourse. We found a disconnect between the discussions that a separate network, bridge tweets, filled by constructing a discourse between th

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Guzmán, Erick Méndez, Ziqi Zhang, and Wasim Ahmed. "Towards understanding a football club’s social media network: an exploratory case study of Manchester United." Information Discovery and Delivery 49, no. 1 (2021): 71–83. http://dx.doi.org/10.1108/idd-08-2020-0106.

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Purpose The purpose of this work is to study how different stakeholders of a football club engage with interactions online through Twitter. It analyses the football club’s Twitter network to discover influential actors and the topic of interest in their online communication. Design/methodology/approach The authors analysed the social networks derived from over two million tweets collected during football matches played by Manchester United. The authors applied social network analysis to discover influencers and sub-communities and performed content analysis on the most popular tweets of the pr

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Jasim, Yaser A., Mustafa G. Saeed, and Manaf B. Raewf. "Analyzing Social Media Sentiment: Twitter as a Case Study." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 4 (2023): 427–50. http://dx.doi.org/10.14201/adcaij.28394.

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This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of t

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Floos, Ahmad Yahya M. "Arabic Rumours Identification By Measuring The Credibility Of Arabic Tweet Content." International Journal of Knowledge Society Research 7, no. 2 (2016): 72–83. http://dx.doi.org/10.4018/ijksr.2016040105.

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Twitter enjoys the fame of the most popular and widely used as a platform for socializing, including all aspects of life current affairs, religious ideas, political issues, scientific research, and general knowledge. Every single activity of day to day life and human behavior and values is lodged at this platform. Sending and receiving messages on Twitter (tweets) with is limited to 140 characters, In this research the author attempts to understand the characteristics of those Arabic rumour (falsified information stream) patterns. False tweets could be a rumour which is mostly recognized as a

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