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Ahmed, S. (2021a). Who inadvertently shares deepfakes? Analyzing the role of political interest, cognitive ability, and social network size. Telematics and Informatics, 57, 101508. https://doi.org/10.1016/j.tele.2020.101508
Ahmed, S. (2021b). Navigating the maze: Deepfakes, cognitive ability, and social media news skepticism. New Media & Society, https://doi.org/10.1177/14614448211019198
Saha, S., & Sim, T. (2020, September). Is Face Recognition Safe from Realizable Attacks?. In 2020 IEEE international joint conference on biometrics (IJCB) (pp. 1-8). IEEE. https://ieeexplore.ieee.org/abstract/document/9304864/
Agarwal, S., El-Gaaly, T., Farid, H., & Lim, S. (2020). Detecting deep-fake videos from appearance and behavior. arXiv preprint. Available at https://arxiv.org/abs/2004.14491
Agarwal, A., Singh, R., Vatsa, M., & Noore, A. (2017). Swapped! digital face presentation attack detection via weighted local magnitude pattern. Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), pp. 659-665.
Ajzen, I., & Fishbein, M. NJ (1980). Understanding attitudes and predicting social behaviour. Prentice-Hall.
Anderson, K. E. (2018). Getting acquainted with social networks and apps: combating fake news on social media. Library HiTech News, 35(3), 1-6.
Archibald, M.M., Ambagtsheer, R.C., Casey, M.G., & Lawless, M. (2019). Using Zoom videoconferencing for qualitative data collection: Perceptions and experiences of researchers and participants. International Journal of Qualitative Methods, 18, 1-8. http//:10.1177/1609406919874596
Arechar, A.A., Gätcher, S., & Molleman, L. (2018). Conducting interactive experiments online. Experimental Economy, 21, 99-131. https://doi.org/10.1007/s10683-017-9527-2.
Arendt, F., & Matthes, J. (2017). Media effects: Methods of hypothesis testing. In P. Rössler, C.A. Hoffner, & van Zoonen, L. (Eds.), The International Encyclopedia of Media Effects (pp. 1-12). http://10.1002/9781118783764.wbieme0024
Atodiresei, C.-S., Tănăselea, A., & Iftene, A. (2018). Identifying fake news and fake users on Twitter. Procedia Computer Science, 126, 451-461. https://doi.org/10.1016/j.procs.2018.07.279
Bautista J.R., & Lin. T.T.C. (2017). Nurses' use of mobile instant messaging applications: A uses and gratifications perspective. International Journal of Nursing Practices, 2017;e12577. https://doi.org/ 10.1111/ijn.12577
Britt, M. A., Rouet, J.-F., Blaum, D., & Millis, K. (2019). A reasoned approach to dealing with fake news. Policy Insights from the Behavioral and Brain Sciences, 6(1), 94-101. https://doi.org/10.1177/2372732218814855
Buendgens-Kosten, J. (2014). Authenticity. ELT Journal, 68(4), 457-459.
Bulger, M. & Davison, P. (2018). The promises, challenges and futures of media literacy. Journal of Media Literacy Education, 10(1), 1-21. Retrieved from https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1365&context=jmle
Busselle, R. W., & Greenberg, B.S. (2000). The nature of television realism judgments: A reevaluation of their conceptualization and measurement. Mass Communication and Society, 3, 249-268. http://10.1207/S15327825MCS0323_05
Cha, J. (2016). Television use in the 21st century: An exploration of television and social television use in a multiplatform environment. First Monday, 21(2). https://doi.org/10.5210/fm.v21i2.6112
Chang, Y. Li, M. & Lyu, S. (2018). In ictu oculi: Exposing ai created fake videos by detecting eye blinking,” Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1-7.
Chen, T., Kumar, A., Nagarsheth, P., Sivaraman, G., & Khoury, E. (2020). Generalization of audio deepfake detection. Proceedings of Odyssey 2020 The Speaker and Language Recognition Workshop, pp. 132-137.
Cho, H., Shen, L. & Wilson, K. (2014). Perceived realism: Dimensions and roles in narrative persuasion. Communication Research, 41(6), 828-851. http:// 10.1177/0093650212450585
Colliander J. (2019). This is fake news': investigating the role of conformity to other users' views when commenting on and spreading disinformation in social media. Computers in Human Behavior, 97, 202-215. https://doi.org/10.1016/j.chb.2019.03.032
Cybenko, A. K., & Cybenko, G. (2018). AI and fake news. IEEE Intelligent Systems, 33(5), 3-7. https://doi.ieeecomputersociety.org/10.1109/MIS.20 18.2877280
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Davison, W.P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47(1), 1-15.
Diakopoulos, N. (2019). Automating the news: How algorithms are rewriting the media. Harvard University Press.
Diakopoulos, N. & Johnson, D. (2021). Anticipating and addressing the ethical implications of deepfakes in the context of elections. New Media & Society, 23(7) 2072-2098. http://doi.org/10.1177/1461444820925811
Dwivedi, Y.K., Rana, N.P., Jeyaraj, A., Clement, M., & Williams, M.D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a revised theoretical model. Information System Frontiers, 21, 719-734. https://doi.org/10.1007/s10796-017-9774-y
Dobber, T., Metoui, N., Trilling, D., Helberger, N., & de Vreese, C. (2020). Do (microtargeted) deepfakes have real effects on political attitudes?” The International Journal of Press/Politics 26, 69-91. https://doi.org/10.1177/1940161220944364
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt, Brace, & Janovich
Eliseev, E.D., & Marsch, E.J. (2021). Externalizing autobiographical memories in the digital age. Trends in Cognitive Science. https://doi.org/10.1016/j.tics.2021.08.005
Enli, G. (2015). Mediated authenticity: How the media constructs reality. New York: Peter Lang.
Figueira, A., & Oliveira, L. (2017). The current state of fake news: challenges and opportunities. Procedia Computer Science, 121, 817-825. https://doi.org/10.1016/j.procs.2017.11.106
Fine, G.A. (2003). Crafting authenticity: The validation of identity in self-taught art. Theory and Society, 32(2),153-80.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley.
Fikse, T.D. (2018). Imagining deceptive deepfakes. An ethnographic exploration of fake videos. Master's thesis. University of Oslo.
Floridi, L. (2018). Artificial Intelligence, deepfakes and a future of ectypes. Philosophy & Technology, 31(3), 317-321.
Gibson, C. B., McDaniel, D., & Szkudlarek, B. (2012). Tales from the (multicultural) field: Toward developing research conducive to proximal theory building. In A. M. Ryan, F. L. Oswald, & F. T. L. Leong (Eds.), Conducting multinational research projects in organizational psychology: Challenges and opportunities (pp. 9-27). Washington, DC: American Psychological Association.
Giorgi, A. (1997). The theory, practice, and evaluation of the phenomenological method as a qualitative research procedure. Journal of Phenomenological Psychology, 28(2), 235-260.
Gottfried, J. (2019, June 14). About three-quarters of Americans favor steps to restrict altered videos and images. Pew Research Center. Available at: https://www.pewresearch.org/fact-tank/2019/06/14/about-three-quarters-of-americans-favor-steps-to-restrict-altered-videos-and-images/
Grady, R.H., Ditto, P.H., & Loftus, E.F. (2021). Nevertheless, partisanship persisted: fake news warnings help briefly, but bias returns with time. Cognitive Research 6, 52. https://doi.org/10.1186/s41235-021-00315-z
Greene, J., Caracelli, V., & Graham, W. (1989). Toward a conceptual framework for mixed method evaluation designs. Educational Evaluation and Policy Analysis, 11, 255-274.
Greenbaum, T. (2003). The gold standard? why the focus group deserves to be the most respected of all qualitative research tools. Quirk's Marketing Research Review, 17, 22-27.
Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2021). Comparing human and machine deepfake detection with affective and holistic processing. arXiv preprint arXiv:2105.06496.
Guest, G., Namey, E., & McKenna, K. (2017). How many focus groups are enough? Building an evidence Base for nonprobability sample sizes. Field Methods, 29(1), 3-22. http:// 10.1177/1525822X16639015
Gundumogula. M. (2020). Importance of focus groups in qualitative research. International Journal of Humanities and Social Science, 8(11), 299-302. http://10.24940/theijhss/2020/v8/i11/HS2011-082
Gunther, A. C., Perloff, R. M., & Tsfati, Y. (2007). Public opinion and the third-person effect. In W. Donsbach & M. Traugott (eds.), The SAGE Handbook of Public Opinion (pp. 184-191). Sage.
Hall, A. (2009). Realism and reality TV. In R. L. Nabi & M.B. Oliver (eds.), The SAGE Handbook of Media Processes and Effects (pp. 423-438). Sage.
Hancock, J.T. & Bailenson, J.N. (2021). The social impact of deepfakes. Cyberpsychology, Behavior, and Social Networking, 24(3). https://doi.org/10.1089/cyber.2021.29208.jth
Hancock, J.T. & Bailenson, J.N. (2021). The social impact of deepfakes. Cyberpsychology, Behavior, and Social Networking, 24(3). https://doi.org/10.1089/cyber.2021.29208.jth
Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89-100. http://10.1093/jcmc/zmz022
Haut, K., Wohn, C., & Antoni, V. (2021). Could you become more credible by being white? Assessing impact of race on credibility with deepfakes. arxiv.org/pdf/2102.08054.pdf
Hennink, M.M., Kaiser, B.N., & Weber, M.B. (2019). What influences saturation? Estimating sample sizes in focus group research. Qualitative Health Research, 29(10), 1483-1496. https://doi.org/10.1177/1049732318821692th
Hogan, J.W., Roy, J., & Korkontzelou, C. (2004). Handling drop-out in longitudinal studies. Statistics in Medicine, 23, 1455-1497. http://10.1002/sim.1728
Holloway, I., & Wheeler, S. (2010). Qualitative research in nursing and healthcare. Wiley-Blackwell.
Hwang, Y., Ryu, J.Y., & Jeong, S-H. (2021). Effects of disinformation using deepfake: The protective effect of media literacy education. Cyberpsychology, Behavior, and Social Netwoking, 24(3), 188-193. https://doi.org/10.1089/cyber.2020.0174
Iacobucci, S., De Cicco, R., Michetti, F., Palumbo, R., & Pagliaro, S. (2021). Deepfakes unmasked: The effects of information priming and bullshit receptivity on deepfake recognition and sharing intention. Cyberpsychology, Behavior, and Social Netwoking, 24(3), 194-202. https://doi.org/10.1089/cyber.2020.0149
Jang, S.M., & Kim, J.K. (2018). Third person effects of fake news: Fake news regulation and media literacy interventions. Computers in Human Behavior, 80, 295-302. https://doi.org/10.1016/j.chb.2017.11.034
Jones, B. (2016). Authenticity in political discourse. Ethical Theory and Moral Practice, 19(2), 489-504.
Johnson, R.B., & Christensen, L.B. (2017). Educational research: Quantitative, qualitative, and mixed approaches. 6th edition. Sage.
Kaplowitz, M., & Hoehn, J. (2001). Do focus groups and individual interviews reveal the same information for natural resource valuation? Ecological Economics, 36, 237-247.
Karasavva, V. & Noorbhai, A. (2021). The real threat of deepfake pornography: A review of Canadian Policy. Cyberpsychology, Behavior, and Social Netwoking, 24(3), 203-209. https://doi.org/10.1089/cyber.2020.0272
Katz, E., Blumler, J. G., & Gurevitch, M. (1974). Utilization of mass communication by the individual. In J. G. Blumler and E. Katz (Eds.), The uses of mass communications: Current perspectives on gratifications research (pp. 19-32). Sage.
Kidd, P., & Parshall, M. (2000). Getting the focus and the group: Enhancing analytical rigor in focus group research. Qualitative Health Research, 10, 293-308.
Kietzmann, H. , Lee, L.W., McCarthy, I.P. Kietzmann, T.C. (2020). Deepfakes: Trick or treat. Business Horizons, 63(2), 135-146. https://doi.org/10.1016/j.bushor.2019.11.006
Kite, J. & Phongsavan, P. (2017). Insights for conducting real-time focus groups online using a web conferencing service, F1000Res, 6 http://10.12688/f1000research.10427.1
Korshunov, P. & Marcel, S. (2020, 7 September). Deepfake detection: humans vs. machine. arXiv:2009.03155v1 [cs.CV] 7 Sep 2020
Korshunov, P. & Marcel, S. (2018). Deepfakes: a new threat to face recognition? assessment and detection. arXiv preprint. Available at https://arxiv.org/abs/1812.08685
Kwok, A.O. J., & Koh, S. G. M. (2021) Deepfake: a social construction of technology perspective, Current Issues in Tourism, 24(13), 1798-1802. http://10.1080/13683500.2020.1738357
Krueger, R., & Casey, M. (2015). Focus groups: A practical guide for applied research (5th ed.). Sage.
Laishram L., Rahman M.M., & Jung S.K. (2021) Challenges and Applications of Face Deepfake. In H. Jeong & K. Sumi (Eds.), Frontiers of Computer Vision. IW-FCV 2021. Communications in Computer and Information Science, 1405. Springer, Cham. https://doi.org/10.1007/978-3-030-81638-4_11
Larrea Estefanía, L. (2015). Estudio sobre la escucha de la voz del locutor con y sin su imagen: análisis del proceso perceptivo y cognitivo del mensaje. PhD thesis. Barcelona: Universitat Pompeu Fabra.
Lee, E-J. (2020). Authenticity model of (mass-oriented) computer-mediated communication: Conceptual explorations and testable propositions. Journal of Computer-Mediated Communication, 25(1), 60-73. https://doi.org/10.1093/jcmc/zmz025
Lederman, R.P. (1993) Comparative and consistent approaches to exploratory research." American Journal of Maternal Child Nursing, 18(2), 107.
Lindholm, C. (2008). Culture and authenticity. Blackwell.
Luo, M., Hancock, J. T., & Markowitz, D. M. (2020). Credibility perceptions and detection accuracy of fake news headlines on social media: Effects of truth-bias and endorsement cues. Communication Research, Advance online publication, 1-25. http://10.1177/0093650220921321
Lynn, P. (2009). Methodology of longitudinal surveys. John Wiley & Sons.
Lynn, P., Couper, M. and Watson, N. (2019) Longitudinal surveys. Unique opportunities and unique methodological challenges. Longitudinal & Life Course Studies, 10(4), 415-420. http:// 10.1332/175795919X15683588414527
Ma, H., Yi, H., Tao, J., Bai, Y., Tian, Z., & Wang, C. (2021, 15 April). Continual learning for fake audio detection. arXiv:2104.07286v1 [cs.SD]
Mao, A., Chen, Y., Gajos, K.Z., Parkes, D.C., Procaccia, A.D., Zhang, H. (). TurkServer: enabling synchronous and longitudinal online experiments. Human Computation AAAI Technical Report WS-12-08, 1-39.
Maertens, R., Roozenbeek, J., Basol, M. & van der Linden, S. (2020). Journal of Experimental Psychology: Applied, 1-16. http://dx.doi.org/10.1037/xap0000315
Metzger, M.J., Flanagin, A.j., Eyal, K., Lemus, D.R., & Mccann, R.M. (2003). Credibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment. Annals of the International Communication Association, 27(1), 293-335, http:// 10.1080/23808985.2003.11679029
Molleda, J.C. (2012). Authenticity and the construct's dimensions in public relations and communication research. Journal of Communication Management,14(3):223-36.
Montserrat, D.M., Hao, H., Yarlagadda, S.K., Baireddy, S. Shao, R., Horvath, J., Bartusiak, E., Yang, J., Guera, D., Zhu, F., & Delp, E.J. (2020). Deepfakes detection with automatic face weighting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2851-2859.
Morgan, D.L. (1997). Focus groups as qualitative research. Second edition. Sage.
Müller, N.M., Markert, K., & Bottinger, K. (2021, July 20). Human perception of audio deepfakes. arXiv:2107.09667v1 [cs.HC]. Available at https://arxiv.org/pdf/2107.09667.pdf
Murphy, G., & Flynn, E. (2021, online). Deepfake false memories. Memory. https://doi.org/10.1080/09658211.2021.1919715
Newman, A., Bavik, Y.L., Mount, M., & Shao, B. (2021). Data collection via online platforms: Challenges and recommendations for future research. Applied Psychology, 70(3), 1380-1402. https://doi.org/10.1111/apps.12302
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019). Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2307-2311). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.2019.8682602
Oliver, R.L. (1977). Effect of expectation and disconfirmation on postexposure product evaluations: an alternative interpretation. Journal of Applied Psychology, 62(4), 480-486. https://doi.org/10.1037/0021-9010.62.4.480
Oliver, R.L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.1177/002224378001700405
Onwuegbuzie, A. J., & Johnson, R.B. (2006). The “validity” issue in mixed research. Research in the Schools, 13, 48-63.
Palan, S., & Schitter, C. (2021). Prolific.ac. A subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17, 22-27. https://doi.org/10.1016/j.jbef.2017.12.004
Palmgreen, P., Wenner, L. A., & Rosengren, K. E. (1985). Uses and gratifications research: The past ten years. In K. E. Rosengren, L. A. Wenner, & P. Palmgreen (Eds.), Media gratifications research: Current perspectives (pp. 11-37). Sage.
Paris, B., & Donovan, J. (2019). Deepfakes and cheap fakes. The manipulation of audio and visual evidence. Data & Society. Retrieved from: https://datasociety.net/wp-content/uploads/2019/09/DS_Deepfakes_Cheap_FakesFinal-1-1.pdf
Perks, L.G., & Turner, J.S. (2019). Podcasts and productivity: A qualitative Uses and Gratifications Study. Mass Communication and Society, 22(1), 96-116. https://doi.org/10.1080/15205436.2018.1490434
Pink, S., & Morgan, J. (2013). Short-term ethnography: Intense routes to knowing. Symbolic Interaction, 36, 351-361. https://doi.org/10.1002/symb.66.
Polit, D.F., & Beck, C.T. (2012). Nursing research: generating and assessing evidence for nursing practice (9th ed.). Lippincott, Williams & Wilkins.
Popova, L. (2010). Perceived reality of media messages: Concept explication and testing. Unpublished doctoral dissertation. Santa Barbara: University of California, Santa Barbara.
Rabinovich, A., Morton, T.A., Birney, M.E., (2012). Communicating climate science: The role of perceived communicator's motives. Journal of Environmental Psychology, 32(1),11-8.
Reid-Searl, K., & Happell, B. (2012). Supervising nursing students administering medication: a perspective from registered nurses. Journal of Clinical Nursing, 21(13/14), 1998-2005.
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2019). FaceForensics++: Learning to detect manipulated facial images. Proceedings of the International Conference on Computer Vision (ICCV), pp. 1-11. Available at https://openaccess.thecvf.com/content_ICCV_2019/html/Rossler_FaceForensics_Learning_to_Detect_Manipulated_Facial_Images_ICCV_2019_paper.html
Rubin, A. M. (2002). The uses-and-gratifications perspective of media effects. In J. Bryant & D. Zillmann (Eds.), Media effects: Advances in theory and research (pp. 525-548). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Rubin, R.B., Palmgreen, P., & Sypher, H.E. (2004a). Source credibility scale - McCroskey. Communication Research Methods. Routledge.
Rubin, R.B., Palmgreen, P., & Sypher, H.E. (2004b). Source credibility scale - Berlo. Communication Research Methods. Routledge.
Rubin, R.B., Palmgreen, P., & Sypher, H.E. (2004b). Perceived realism scale - Berlo. Communication Research Methods. Routledge.
Sahidullah, M., Delgado, H., Todisco, M., Kinnunen, T., Evans, N., Yamagishi, J., Lee, K-A. (2018). Introduction to voice presentation attack detection and recent advances. In Marcel, S., Nixon, M.S., Fierrez, J., Evans, N. (Eds.), Handbook of Biometric Anti-Spoofing: Presentation Attack Detection (pp. 1-44). Springer. Available at http://cs.joensuu.fi/pages/tkinnu/webpage/pdf/voicePAD-springer2018.pdf
Schoonenboom, J., & Johnson, R.B. (2017). How to construct a mixed methods research design. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 69, 107-131. http://10.1007/s11577-017-0454-1
Scolari, C.A. (ed.) (2018). Teens, media and collaborative cultures: exploiting teens' transmedia skills in the classroom. Barcelona: Transmedia Literacy H2020 Research and Innovation Action/Universitat Pompeu Fabra. ISBN: 978 84 697 9843 0
Scolari, C.A., Masanet, M-J., Guerrero-Pico, M., & Establés, M-J. (2018). Transmedia literacy in the new media ecology: Teens' transmedia skills and informal learning strategies. El Profesional de la Información, 27(4), 801-812. https://doi.org/10.3145/epi.2018.jul.09
Self, C.C., & Roberts, C. (2019). Credibility. An Integrated Approach to Communication Theory and Research. Routledge.
Soto-Sanfiel, M. T. (2015). The creative manipulation of time through digital personal narratives. In Springer Series on Cultural Computing (pp. 75-90) https://doi.org/10.1007/978-1-4471-6681-8_5
Soto-Sanfiel, M.T. (2008a). Efecto del tono de voz y de la percepción del rostro en la formación de impresiones sobre los hablantes mediáticos. Comunicación y Sociedad, 10(junio-diciembre), 129-161.
Soto-Sanfiel, M.T. (2008b). Impresiones sobre los hablantes mediáticos a partir de la prfesionalidad en su elocución y el contenido de su discurso. Signo y Pensamiento, 53, 246-266.
Soto-Sanfiel, M.T. (2000). Influencia de la percepción visual del rostro en la credibilidad de la voz. PhD thesis. Barcelona: Universitat Autònoma de Barcelona.
Soto-Sanfiel, M.T., & Angulo-Brunet, A. (2020). How European adolescents get engaged with films?: Psychometric properties of the narrative engagement scale. Profesional de la información, 29(5), e290502. https://doi.org/10.3145/epi.2020.sep.02
Soto-Sanfiel, M.T., Villegas-Simón, I., Angulo-Brunet, A. (2018a). Film literacy in secondary schools across Europe: A comparison of five countries' responses to an educational project on cinema. International Journal of Media & Cultural Politics, 14(2), 187-213. https://doi.org/10.1386/macp.14.2.187_1
Soto-Sanfiel, M.T., Villegas-Simón, I., Angulo-Brunet, A. (2018b). Youngsters and cinema in the European Union: A cross-cultural study on their conceptions and knowledge about cinema. International Communication Gazette, 80(8), 714-745. https://doi.org/10.1177/1748048518759171
Soto-Sanfiel, M.T., Villegas-Simón, I., Angulo-Brunet, A. (2021). Correlational network visual analysis of adolescents' film entertainment responses. Communication & Society, 34(1). https://doi.org/10.15581/003.34.1.157-175
Spivak, R. (2019). Deepfakes: The newest way to commit one of the oldest crimes. The Georgetown Law Technology Review, 3(2), 339-400.
Stebbins, R.A. (2001). Exploratory research in the social sciences. Sage.
Stiers, D., Larner, J., Kenny, J. et al. (2021). Candidate authenticity: 'To thine own self be true'. Political Behavior, 43, 1181-1204 https://doi.org/10.1007/s11109-019-09589-y
Štulhofer, A., Matković, T., Kohut, T., Koletić, G., Buško, V., Landripet, I., & Vodopijevec, A. (2021). Are we losing the most relevant cases first? Selective dropout in two longitudinal studies of adolescent pornography use. Archives of Sexual Behaviour, 50, 2215-2226. https://doi.org/10.1007/s10508-021-01931-y
Sun, Y., Pan, Z., & Shen, L. (2008). Understanding the third-person perception: Evidence from a meta-analysis. Journal of Communication, 58, 280-300. http:¬¬¬//10.1111/j.1460-2466.2008.00385.x
Sundar, S. S. (2000). Multimedia effects on processing and perception of online news: A study of picture, audio, and video downloads. Journalism and Mass Communication Quarterly, 77(3), 480-499. http://10.1177/107769900007700302
Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. J. Metzger & A. J. Flanagin (Eds.), Digital Media, Youth, and Credibility (pp. 72-100). The MIT Press.
Sundar, S.S., Molina, M.M., & Cho, E. (2021). Seeing is believing: Is video modality more powerful in spreading fake news via online messaging apps? Journal of Computer-Mediated Communication, 1-19. http://10.1093/jcmc/zmab010
Sykes, B.L., Verma, A. Hancock, B.H. (2018). Aligning sampling and case selection in quantitative-qualitative research designs: Establishing generalizability limits in mixed-method studies. Ethnography, 19(2) 227-253. http:// 10.1177/1466138117725341
Tahir,R., Batool, B., Jamshed, H., Jameel, M., Anwar, M., Ahmed, F., Zafar, M.A., & Zafar, M.F. (2021). Seeing is believing: Exploring perceptual differences in deepfake videos. Proceedings of the CHI '21, May 8-13, 2021. https://doi.org/10.1145/3411764.3445699
Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation, 27(2), 237-246.
Venkatesh, V., Thong, J.Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
Wahl-Jorgensen, K. & Carlson, M. (2021). Conjecturing fearful futures: Journalistic discourses on deepfakes, Journalism Practice, 15(6), 803-820. http://doi.org/10.1080/17512786.2021.1908838
Wang, S-Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A. (2020). Cnngenerated images are surprisingly easy to spot...for now. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_CNN-Generated_Images_Are_Surprisingly_Easy_to_Spot..._for_Now_CVPR_2020_paper.html
Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A. et al. (2010). ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech. Computer Speech and Language, 64, pp.101114. ff10.1016/j.csl.2020.101114ff. ffhal-02945493
Weisman, W.D. & Peña, J.F. (2021). Face the uncanny: The effects of doppelganger talking head avatars on affect-based trust toward artificial intelligence technology are mediated by uncanny valley perceptions. Cyberpsychology, Behavior, and Social Netwoking, 24(3), 182-187. https://doi.org/10.1089/cyber.2020.0175
Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology Innovation Management Review, 9(11), 39-52. http://doi.org/10.22215/timreview/12
White, H. & Sabarwal, S. (2014). Quasi-experimental design and methods. UNICEF.
Wright, S. (2021). Beyond fake news? A longitudinal analysis of how Australian politicians attack and criticise the media on Twitter. Journal of Language & Politics, 20(5), 719-740. https://doi.org/10.1075/jlp.21027.wri
Wu, F., Ma, Y., & Zhang, Z. (2021). I found a more attractive deepfaked self: The self-enhancement effect in deepfake video exposure. Cyberpsychology, Behavior, and Social Netwoking, 24(3), 173-181. https://doi.org/10.1089/cyber.2020.0173
Yang, X., Li, Y., & Lyu, S. (2019). Exposing deep fakes using inconsistent head pose. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8261-8265.
Zhang, Y., Zheng, L., & Thing,V.L.L. (2017). Automated face swapping and its detection. Proceedings of the IEEE International Conference on Signal and Image Processing (ICSIP), pp. 15-19.
Zibrek, K., Kokkinara, E., & McDonnell, R. (2018). The effect of realistic appearance of virtual characters in immersive environments - Does the character's personality play a role? IEEE Transactions on visualization and computer graphics, 24(4), 1681-1690.