rtweet_citations

A repository for tracking citations/references/uses of rtweet in published articles because (a) people aren’t always great about using appropriate citations for R packages, (b) Google scholar hasn’t been great at locating/tracking rtweet citations, and (c) I’d like to one day get tenure :).

Bibliography list

62.   Xu S, Zhou A (2020). “Hashtag homophily in twitter network: Examining a controversial cause-related
        marketing campaign.” Computers in Human Behavior, 102, 87-96.

61.   Zhang X, Mu L (2020). “Incorporating Online Survey and Social Media Data into a GIS Analysis for
        Measuring Walkability.” In Geospatial Technologies for Urban Health, 133-155. Springer.

60.   Aglionby G, Davis CI, Mishra P, Caines A, Yannakoudakis H, Rei M, Shutova E, Buttery P (2019).
        “CAMsterdam at SemEval-2019 Task 6: Neural and graph-based feature extraction for the identification
        of offensive tweets.” In Proceedings of the 13th International Workshop on Semantic Evaluation,
        556-563.

59.   Agrawal T, Singhal A (2019). “An Efficient Knowledge-Based Text Pre-processing Approach for Twitter
        and Google+.” In International Conference on Advances in Computing and Data Sciences, 379-389.
        Springer.

58.   Boot AB, Sang ETK, Dijkstra K, Zwaan RA (2019). “How character limit affects language usage in
        tweets.” Palgrave Communications, 5(1), 76.

57.   Bradley A, James RJ (2019). “How are major gambling brands using Twitter?” International
        Gambling Studies, 1-20. doi: 10.1080/14459795.2019.1606927 (URL: https://doi.org/
        10.1080/14459795.2019.1606927).

56.   Burton JW, Cruz N, Hahn U (2019). “How Real is Moral Contagion in Online Social Networks?” In
        Proceedings of the Cognitive Science Society.

55.   Burton N (2019). “Exploring user sentiment towards sponsorship and ambush marketing.” International
        Journal of Sports Marketing and Sponsorship.

54.   Garcia-Rudolph A, Laxe S, Saurí J, Guitart MB (2019). “Stroke Survivors on Twitter: Sentiment and
        Topic Analysis From a Gender Perspective.” Journal of medical Internet research, 21(8), e14077.

53.   Georgakopoulos SV, Tasoulis SK, Vrahatis AG, Plagianakos VP (2019). “Convolutional Neural Networks
        for Twitter Text Toxicity Analysis.” In INNS Big Data and Deep Learning conference, 370-379.
        Springer.

52.   Gitto S, Mancuso P (2019). “Brand perceptions of airports using social networks.” Journal of Air
        Transport Management, 75, 153 - 163. ISSN 0969-6997, doi: 10.1016/j.jairtraman.2019.01.010 (URL:
        https://doi.org/10.1016/j.jairtraman.2019.01.010), <URL: http://www.sciencedirect.com/science/
        article/pii/S0969699718303144>.

51.   Gray AA (2019). Brands Take a Stand for Good: The Effect of Brand Activism on Social Media
        Engagement. Senior Honors Thesis, University of New Hampshire, Durham.

50.   Greco F, Polli A, others (2019). “Vaccines in Italy: the emotional text mining of social media.”
        RIEDS-Rivista Italiana di Economia, Demografia e Statistica-Italian Review of Economics, Demography
        and Statistics, 73(1), 89-99.

49.   Heft A (2019). “The Panama Papers investigation and the scope and boundaries of its networked
        publics: Cross-border journalistic collaboration driving transnationally networked public spheres.”
        Journal of Applied Journalism & Media Studies, 8(2), 191-209.

48.   Hunt K, Gruszczynski M (2019). “The influence of new and traditional media coverage on public
        attention to social movements: the case of the Dakota Access Pipeline protests.” Information,
        Communication & Society, 1-17.

47.   Jones NM, Silver RC (2019). “This is not a drill: Anxiety on Twitter following the 2018 Hawaii false
        missile alert.” American Psychologist.

46.   Justice JW, Bricker BJ (2019). “Hacked: Defining the 2016 Presidential Election in the Liberal
        Media.” Rhetoric and Public Affairs, 22(3), 389-420.

45.   Kearney MW (2019). “Analyzing change in network polarization.” New Media & Society. doi:
        10.1177/1461444818822813 (URL: https://doi.org/10.1177/1461444818822813), [Online First], <URL:
        10.1177/1461444818822813>.

44.   Larsen EG, Fazekas Z (2019). “Quantitative Politics with R.” NA.

43.   Li TR, Chamrajnagar A, Fong X, Rizik N, Fu F (2019). “Sentiment-based prediction of alternative
        cryptocurrency price fluctuations using gradient boosting tree model.” Frontiers in Physics, 7,
        98. doi: 10.3389/fphy.2019.00098 (URL: https://doi.org/10.3389/fphy.2019.00098).

42.   Lutkenhaus RO, Jansz J, Bouman MP (2019). “Tailoring in the digital era: Stimulating dialogues
        on health topics in collaboration with social media influencers.” DIGITAL HEALTH, 5,
        2055207618821521. doi: 10.1177/2055207618821521 (URL: https://doi.org/10.1177/2055207618821521).

41.   Lutkenhaus RO, Jansz J, Bouman MP (2019). “Mapping the Dutch Vaccination Debate on Twitter:
        Identifying Communities, Narratives, and Interactions.” Vaccine: X, 100019.

40.   M’Bareck ML (2019). Political Speech on Twitter: A Sentiment Analysis of Tweets and News Coverage
        of Local Gun Policy. Ph.D. thesis, University of Arkansas.

39.   Noaeen M, Far BH (2019). “Social media analysis for traffic management.” In Proceedings of the 14th
        International Conference on Global Software Engineering, 72-73. IEEE Press.

38.   Prieu C (2019). “Changing Faces of Morphological Innovation in French: Gender-Marking in Feminist
        Discourse on Twitter.” In 49th Linguistic Symposium on Romance Languages in Spring 2019.

37.   Rekik A, Jamoussi S, Hamadou AB (2019). “Violent Vocabulary Extraction Methodology: Application to
        the Radicalism Detection on Social Media.” In International Conference on Computational Collective
        Intelligence, 97-109. Springer.

36.   Sansone A, Cignarelli A, Ciocca G, Pozza C, Giorgino F, Romanelli F, Jannini EA (2019). “The
        Sentiment Analysis of Tweets as a New Tool to Measure Public Perception of Male Erectile and
        Ejaculatory Dysfunctions.” Sexual Medicine.

35.   Bakar MAA, Ariff NM, Hui EX (2018). “Exploratory data analysis of Twitter’s rhythm in Malaysia.” In
        AIP Conference Proceedings, volume 2013 number 1, 020056. AIP Publishing.

34.   Bossetta M (2018). “A simulated cyberattack on Twitter: Assessing partisan vulnerability to spear
        phishing and disinformation ahead of the 2018 U.S. midterm elections.” First Monday, 23(12).
        ISSN 13960466, doi: 10.5210/fm.v23i12.9540 (URL: https://doi.org/10.5210/fm.v23i12.9540), <URL:
        https://firstmonday.org/ojs/index.php/fm/article/view/9540>.

33.   Buscema M, Ferilli G, Massini G, Zavarrone E (2018). “Media content analysis on online hate
        speech.” Positive Messengers. <URL: https://positivemessengers.net/images/library/pdfs/
        Media_content_analysis_form_eng.pdf>.

32.   Cantos Sancho A (2018). Estudio de Nuevas Herramientas en la Respuesta del Consumidor. Ph.D.
        thesis, Universitat Politécnica De Valéncia.

31.   Díez MM, Palacio V, Principe O, Gaztelumendi S (2018). “Palabras clave en twiter de centros
        meteorológicos.” Acta de las Jornadas Científicas de la Asociación Meteorológica Española,
        1(35).

30.   Doceka D (2018). “Selfies as a mental disorder, escaped biometric database and tax optimization of
        Google.” Lupa. <URL: https://www.lupa.cz/clanky/selfies-jako-dusevni-porucha-unikla-biometricka-
        databaze-a-danove-optimalizace-googlu/>.

29.   Erlandsen M (2018). “Twitter as a tool of para-disploomacy: An exploratory cohort study based
        on Catalonia (2013-2017).” Revista Chilena de Relaciones Internacionales, 2, 211-231. <URL:
        https://rchri.cl/wp-content/uploads/2018/04/211-231.pdf>.

28.   Greenhalgh SP (2018). Spaces and their social frontiers: Using community dimensions to distinguish
        between teacher-focused hashtags on Twitter. Ph.D. thesis, Michigan State University.

27.   González F, Medina V (2018). “Shaping the public sphere: The politics of fictional expectations in
        social media.” working paper.

26.   Jann O, Schottmuller C (2018). “Breakdown of debate and the usefulness of echo chambers: Theory
        and evidence.” working paper, <URL: https://editorialexpress.com/cgi-bin/conference/download.cgi?
        db_name=EEAESEM2018&paper_id=2395>.

25.   Kearney MW (2018). Analyzing tweets about the 2016 US presidential “blunder” election. ABC-CLIO.

24.   Krsová L (2018). Czech journalists on Twitter: Analysis of social interactions of the Czech media
        space. Master’s thesis, Univerzita Karlova.

23.   Ku T, Xu S, Li W, Yuan B, others (2018). “Affective Emotional Component Analysis: Text Mining Based
        on Social Network.” OSF Preprints. doi: 10.31219/osf.io/tpuw3 (URL: https://doi.org/10.31219/
        osf.io/tpuw3).

22.   Lacroix D (2018). Tweeting populist sentiment: A study of Forum voor Democratie’s use
        of emotional language on Twitter. Ph.D. thesis, University of Amsterdam. <URL: http://
        www.scriptiesonline.uba.uva.nl/document/666363>.

21.   Molyneux L, Lewis SC, Holton AE (2018). “Media work, identity, and the motivations that shape
        branding practices among journalists: An explanatory framework.” New Media & Society, 1-20. doi:
        10.1177/1461444818809392 (URL: https://doi.org/10.1177/1461444818809392).

20.   Rottigni E (2018). Fragile cities: how Venice and Barcelona communicate their need for
        sustainability. B.S. thesis, Università Ca’Foscari Venezia.

19.   Rudis B (2018). 21 recipes for mining Twitter with rtweet. rud.is. <URL: https://rud.is/books/21-
        recipes/>.

18.   Štědroňová J (2018). “Inkluzivní povaha Twitterové komunikace politik: je Twitter skutečně
        demokratizující systém?” Univerzita Karlova, Filozofická fakulta.

17.   Tasoulis SK, Vrahatis AG, Georgakopoulos SV, Plagianakos VP (2018). “Real time sentiment change
        detection of Twitter data streams.” arXiv:1804.00482.

16.   Thorson AA (2018). Social networks & price forecasting: The case of Bitcoins. Bachelor’s Degree,
        University of Barcelona.

15.   Tomohira N, Wakamatsu H (2018). “On the use of adjectives of”different" and its distribution." In
        Proceedings of the 24th Annual Conference of the Society of Language Processing.

14.   Tsoi KK, Zhang L, Chan NB, Chan FC, Hirai HW, Meng HM (2018). “Social media as a tool to look for
        people with dementia who become lost: Factors that matter.” In Proceedings of the 51st Hawaii
        International Conference on System Sciences.

13.   Tsoi KK, Chan NB, Chan FC, Zhang L, Lee AC, Meng HM (2018). “How can we better use Twitter to find a
        person who got lost due to dementia?” npj Digital Medicine, 1(1), 14.

12.   Ueda A (2018). SNS political advertisement communication: Building relationship between voters
        and politicians in election environment in Japan. Master’s thesis, Kyoto University. <URL: http://
        hdl.handle.net/2433/229491>.

11.   Unsihuay JEG (2018). “Topic modeling en datos de Twitter: Una aplicación en el contexto político
        peruano.” XXVIII Simposio Internacional de Estadístic.

10.   Wu H, Ying S (2018). “Finding Similar Users over Multiple Attributes on the Basis of Intuitionistic
        Fuzzy Set.” Mobile Networks and Applications, 1-9.

9.     Akitsune K, Suzuki T (2017). Network Analysis, series Learning with R Data Science, 2 edition.
        Kyoritsu Shuppan.

8.     Fitzgerald JD (2017). “Sentiment analysis of (you guessed it!) Donald Trump’s tweets.” Storybench.
        <URL: http://www.storybench.org/sentiment-analysis-of-you-guessed-it-donald-trumps-tweets/>.

7.     Kearney MW (2017). A network-based approach to estimating partisanship and analyzing change in
        polarization during the 2016 general election. Ph.D. thesis, University of Kansas.

6.     Lanzetta VB (2017). R data visualization recipes: A cookbook with 65+ data visualization recipes
        for smarter decision-making. Packt Publishing Ltd.

5.     Mandal JK, Dutta P, Mukhopadhyay S (2017). Computational intelligence, communications, and business
        analytics: First international conference, CICBA 2017, Kolkata, India, March 24-25, 2017, Revised
        Selected Papers, volume 775. Springer.

4.     Sinha R, Kumar M, Goswami S (2017). “An approach to build a database for crimes in India using
        Twitter.” In International Conference on Computational Intelligence, Communications, and Business
        Analytics, 150-160. Springer.

3.     Tancoigne E (2017). “Four things Twitter tells us about”Citizen Science" (and 1,000 things
        it doesn’t)." Citizen Sciences: Rethinking Science and Public Participation. <URL: http://
        citizensciences.net/2017/01/26/4-things-twitter-tells-us-about-citizen-science/>.

2.     Valls F, Redondo E, Fonseca D, Torres-Kompen R, Villagrasa S, Martí N (2017). “Urban data and
        urban design: A data mining approach to architecture education.” Telematics and Informatics. doi:
        10.1016/j.tele.2017.09.015 (URL: https://doi.org/10.1016/j.tele.2017.09.015).

1.     Porcu V (2016). Text mining e sentiment analysis con R. Valentina Porcu.

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