Academic literature on the topic 'Twitter behaviour'
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Journal articles on the topic "Twitter behaviour"
ALGARNI, ABDULLAH AHMED. "THE ROLE OF SOCIAL NETWORKS IN PROMOTING POSITIVE BEHAVIOUR DURING COVID-19 CRISIS AMONGST A SAMPLE OF USERS IN SAUDI SOCIETY: TWITTER AS A MODEL." AD ALTA: 12/01 12, no. 1 (June 30, 2022): 7–16. http://dx.doi.org/10.33543/1201716.
Full textKlotz, Christine, Coskun Akinalp, and Herwig Unger. "Clustering user behaviour patterns on Twitter." International Journal of Social Network Mining 2, no. 3 (2016): 203. http://dx.doi.org/10.1504/ijsnm.2016.082632.
Full textAkinalp, Coskun, Christine Klotz, and Herwig Unger. "Clustering user behaviour patterns on Twitter." International Journal of Social Network Mining 2, no. 3 (2016): 203. http://dx.doi.org/10.1504/ijsnm.2016.10003518.
Full textGabriel, T. Nyitse, Terhile Agaku, and Cordelia Clayton. "Influence of Social Media on Abuja Youths Behaviour in 2019 General Elections: A Study of Twitter." RESEARCH JOURNAL OF MASS COMMUNICATION AND INFORMATION TECHNOLOGY 8, no. 1 (October 6, 2022): 63–74. http://dx.doi.org/10.56201/rjmcit.v8.no1.2022.pg63.74.
Full textAzwar, Azmiera Izzati, and Suet Nie Kho. "Factors Influencing Young Malaysians’ Political Information Seeking Behaviour on Twitter." Jurnal Komunikasi: Malaysian Journal of Communication 38, no. 3 (September 30, 2022): 87–105. http://dx.doi.org/10.17576/jkmjc-2022-3803-06.
Full textRachmawati, Iva, Machya Astuti Dewi, Frans Richard Kodong, and Panji Dwi Ashrianto. "Trump and Biden's Covid 19 Policy Narrative on Twitter." RSF Conference Series: Business, Management and Social Sciences 1, no. 4 (October 22, 2021): 1–17. http://dx.doi.org/10.31098/bmss.v1i4.280.
Full textHodeghatta, Umesh Rao, and Sangeeta Sahney. "Understanding Twitter as an e-WOM." Journal of Systems and Information Technology 18, no. 1 (March 14, 2016): 89–115. http://dx.doi.org/10.1108/jsit-12-2014-0074.
Full textDÜNDAR, Mahire Armağan, and Fırat TUFAN. "Sosyal Ağ Sitelerinde Sosyal Karşılaştırma Davranışı: Instagram- Twitter Karşılaştırması." Yeni Medya Dergisi 2022, no. 12 (June 30, 2022): 1–16. http://dx.doi.org/10.55609/yenimedya.1051044.
Full textRoenneberg, Till. "Twitter as a means to study temporal behaviour." Current Biology 27, no. 17 (September 2017): R830—R832. http://dx.doi.org/10.1016/j.cub.2017.08.005.
Full textJohnson, Michiel, Steve Paulussen, and Peter Van Aelst. "The manifest and latent functions of Twitter use by journalists: An observational study among economic journalists." Journal of Applied Journalism & Media Studies 8, no. 3 (November 1, 2019): 311–29. http://dx.doi.org/10.1386/ajms_00004_1.
Full textDissertations / Theses on the topic "Twitter behaviour"
Järpehult, Oscar, and Martin Lindblom. "Longitudinal measurements of link usage on Twitter." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159331.
Full textDahlqvist, Veronica. "Why is the bird (re)tweeting? : Creating a simulation of retweeting behaviour on Twitter." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129352.
Full textAlghamdi, Bandar Abdulrahman. "Topic-based feature selection and a hybrid approach for detecting spammers on twitter." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/204112/1/Bandar%20Abdulrahman%20A_Alghamdi_Thesis.pdf.
Full textAlmuhanna, Nora. "Social media acceptance and use under risk : a cross-cultural study of the impact of antisocial behaviour on the use of Twitter." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/419402/.
Full textGaillard, Béatrice. "Optimisation des stratégies de communication durant les épidémies d'arboviroses." Thesis, Montpellier 3, 2019. http://www.theses.fr/2019MON30096.
Full textThe burden of Aedes-borne arbovirus (ie dengue, chikungunya, Zika, yellow fever...) has increased substantially during the last two decades. In the absence of vaccine and / or curative treatment for most of these pathogens, their prevention and control are mainly based on the control of mosquitoes vectors, through integrated strategies of vector control where collective participation is pivotal. Thus, as advocated by WHO that makes social mobilization one of the pillars of the fight against arboviroses, communication plans must be constantly optimized.Therefore, the overall goal of this PhD is to evaluate the influence of prevention messages and the relevance of new media such as Twitter to optimize communication strategies to effectively influence the behavior of exposed populations. This work was carried out in two epidemiological situations: the south of France, where this risk is recent following the invasion of the potential vector Aedes albopictus, and Martinique where the mosquito Aedes aegypti is implemented since decades and can generates an habituation feeling of the inhabitants.In a first part, we aim defining the nature of the most efficient message through interviews with the populations of southern France and Martinique. In a second part, we evaluate the robustness of twitter as a source of revalant information to adapt ni real-time communication strategies. Finally, in a third part, we estimate the feelings as measured on Twitter during an outbreak of Chikungunya and explain, from a sociological point of view, how the different communication strategies performed by the authorities have impacted population feelings.Keywords: social networks, twitter, human behavior, arboviroses, chikungunya
Mowbray, John Alexander. "The role of networking and social media tools during job search : an information behaviour perspective." Thesis, Edinburgh Napier University, 2018. http://researchrepository.napier.ac.uk/Output/1516318.
Full textTran, Hung Viet. "Discovering entities' behavior through mining Twitter." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3545.
Full textNilsson, Olav, and Filip Polbratt. "Counting the clicks on Twitter : A study in understanding click behavior on Twitter." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139702.
Full textJeri-Yabar, Antoine, Alejandra Sanchez-Carbonel, Karen Tito, Jimena Ramirez-delCastillo, Alessandra Torres-Alcantara, Daniela Denegri, and Nilton Yhuri Carreazo. "Association between social media use (Twitter, Instagram, Facebook) and depressive symptoms: Are Twitter users at higher risk?" SAGE Publications Ltd, 2019. http://hdl.handle.net/10757/625046.
Full textBackground: The purpose of this study was to determine the association between social media dependence and depressive symptoms and also, to characterize the level of dependence. It was a transversal, analytical research. Subjects and Methods: The stratified sample was 212 students from a private university that used Facebook, Instagram and/or Twitter. To measure depressive symptoms, Beck Depression Inventory was used, and to measure the dependence to social media, the Social Media Addiction Test was used, adapted from the Internet Addiction Test of Echeburúa. The collected data were subjected for analysis by descriptive statistics where STATA12 was used. Results: The results show that there is an association between social media dependence and depressive symptoms (PR [Prevalence Ratio] = 2.87, CI [Confidence Interval] 2.03–4.07). It was also shown that preferring the use of Twitter (PR = 1.84, CI 1.21–2.82) over Instagram (PR = 1.61, CI 1.13–2.28) is associated with depressive symptoms when compared to the use of Facebook. Conclusion: Excessive social media use is associated with depressive symptoms in university students, being more prominent in those who prefer the use of Twitter over Facebook and Instagram.
Revisión por pares
Njegomir, Nicholas M. "The impact of Facebook, Twitter, and YouTube on Millennials' political behavior." Thesis, Gonzaga University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10118313.
Full textSocial media plays a prominent role in the daily lives of Millennials. The majority of Millennials use some form of social media, and with the amount of political content on various social media sites, it is worth examining how social media influences Millennials’ political behavior. This study focused on three social media sites: Facebook, Twitter, and YouTube. This research was rooted in George Gerbner and Larry Gross’ Cultivation theory (1976), which states that long-term media exposure shapes reality. The study consisted of survey and focus group research, which attempted to determine how much time Millennials spend on each site, how politically active they are, and whether or not they thought their political behavior was influenced by social media. The resulting data showed that YouTube and Twitter were not used for political information, but Facebook is so saturated with political content that it may have a negative influence on formal political participation levels. Millennials may feel that participating in political dialogue on Facebook qualifies as formal political participation.
Books on the topic "Twitter behaviour"
Barnett, Mac. Billy Twitters and his big blue whale problem. New York: Disney Hyperion Books, 2009.
Find full textEhrenreich, Samuel E., and Marion K. Underwood. Peer Coercion and Electronic Messaging. Edited by Thomas J. Dishion and James Snyder. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199324552.013.12.
Full textThomason, Krista K. Ajax Reviled. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190843274.003.0006.
Full textDjupe, Paul A., Anand Edward Sokhey, and Amy Erica Smith. The Knowledge Polity. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197611913.001.0001.
Full textLoney, Alexander C., and Stephen Scully, eds. The Oxford Handbook of Hesiod. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190209032.001.0001.
Full textNielsen, Rasmus Kleis, and Sarah Anne Ganter. The Power of Platforms. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780190908850.001.0001.
Full textauthor, Shapiro-Rieser Rhonda, and Bahrami Yasmin illustrator, eds. The secret rules of social networking. AAPC Publishing, 2015.
Find full textBook chapters on the topic "Twitter behaviour"
Alghamdi, Bandar, Yue Xu, and Jason Watson. "Malicious Behaviour Analysis on Twitter Through the Lens of User Interest." In Communications in Computer and Information Science, 233–49. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0292-3_15.
Full textNarwal, Ramesh, and Himanshu Aggarwal. "Predicting Online Game-Addicted Behaviour with Sentiment Analysis Using Twitter Data." In Lecture Notes in Electrical Engineering, 505–17. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2354-7_45.
Full textWeiss, Fernanda, Ignacio Espinoza, Julio Hurtado, and Marcelo Mendoza. "Claim Behavior over Time in Twitter." In Social Computing and Social Media. Design, Human Behavior and Analytics, 468–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21902-4_33.
Full textGuo, Lixiang, Zhaoyun Ding, and Hui Wang. "Behavior-Based Twitter Overlapping Community Detection." In Database Systems for Advanced Applications, 371–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32055-7_31.
Full textDhall, Abhinav, and Jesse Hoey. "First Impressions - Predicting User Personality from Twitter Profile Images." In Human Behavior Understanding, 148–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46843-3_10.
Full textCagliero, Luca, and Alessandro Fiori. "Analyzing Twitter User Behaviors and Topic Trends by Exploiting Dynamic Rules." In Behavior Computing, 267–87. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2969-1_17.
Full textSung, Juyup, Seunghyeon Moon, and Jae-Gil Lee. "The Influence in Twitter: Are They Really Influenced?" In Behavior and Social Computing, 95–105. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-04048-6_9.
Full textMalave, Nitin, and Sudhir N. Dhage. "Sarcasm Detection on Twitter: User Behavior Approach." In Intelligent Systems, Technologies and Applications, 65–76. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6095-4_5.
Full textHayama, Tessai. "Detecting TV Program Highlight Scenes Using Twitter Data Classified by Twitter User Behavior." In Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems, 1–13. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70019-9_1.
Full textQi, SiHua, Lulwah AlKulaib, and David A. Broniatowski. "Detecting and Characterizing Bot-Like Behavior on Twitter." In Social, Cultural, and Behavioral Modeling, 228–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93372-6_26.
Full textConference papers on the topic "Twitter behaviour"
Orozco, Albert, Sacha Levy, and Reihaneh Rabbany. "Graph Neural Networks Learn Twitter Bot Behaviour." In LatinX in AI at Neural Information Processing Systems Conference 2020. Journal of LatinX in AI Research, 2020. http://dx.doi.org/10.52591/lxai2020121213.
Full textKekulluoglu, Dilara, Kami Vaniea, and Walid Magdy. "Understanding Privacy Switching Behaviour on Twitter." In CHI '22: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3491102.3517675.
Full textBello, Bello Shehu, Reiko Heckel, and Leandro Minku. "Reverse Engineering the Behaviour of Twitter Bots." In 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2018. http://dx.doi.org/10.1109/snams.2018.8554675.
Full textKhazaei, Taraneh, Lu Xiao, Robert Mercer, and Atif Khan. "Privacy Behaviour and Profile Configuration in Twitter." In the 25th International Conference Companion. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2872518.2890088.
Full textBello, Bello Shehu, and Reiko Heckel. "Analyzing the Behaviour of Twitter Bots in Post Brexit Politics." In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2019. http://dx.doi.org/10.1109/snams.2019.8931874.
Full textBlanco-Herrero, David, Patricia Sánchez-Holgado, Javier J. Amores, Maximiliano Frías-Vázquez, and Carlos Arcila-Calderón. "News media behaviour in Twitter during a major political event." In TEEM'20: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3434780.3436674.
Full textPerez Cabañero, Carmen, Enrique Bigne, Carla Ruiz Mafe, and Antonio Carlos Cuenca. "Sentiment Analysis of Twitter in Tourism Destinations." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11621.
Full textFeatherstone, Coral. "South African bot behaviour post the July 2018 Twitter account cull." In 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2019. http://dx.doi.org/10.1109/icabcd.2019.8851039.
Full textEl Houda Ben Chaabene, Nour, Amel Bouzeghoub, Ramzi Guetari, and Henda Hajjami Ben Ghezala. "Applying Machine Learning Models for Detecting and Predicting Militant Terrorists Behaviour in Twitter." In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2021. http://dx.doi.org/10.1109/smc52423.2021.9659253.
Full textGuimarães, Nuno, Álvaro Figueira, and Luís Torgo. "Contributions to the Detection of Unreliable Twitter Accounts through Analysis of Content and Behaviour." In 10th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006932800920101.
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