Screenomics: A Framework to Capture and Analyze Personal Life Experiences and the Ways that Technology Shapes Them

References

• Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514. doi:10.1126/science.aaa1465
   PubMed Web of Science ®Google Scholar
• Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008, February). Finding high-quality content in social media. In Proceedings of the 2008 international conference on web search and data mining (pp. 183–194). New York, NY: ACM.
   Google Scholar
• Aharony, N., Pan, W., Ip, C., Khayal, I., & Pentland, A. (2011). Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing, 7(6), 643–659. doi:10.1016/j.pmcj.2011.09.004
   Web of Science ®Google Scholar
• Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236. doi:10.1257/jep.31.2.211
   Web of Science ®Google Scholar
• Anderson, J., & Rainie, L. (2012, February 29). Millennials will benefit and suffer due to their hyperconnected lives. Pew Research Center. Retrieved from https://s3.amazonaws.com/academia.edu.documents/30881000/PIP_Future_of_Internet_2012_Young_Brains_PDF.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1509479686&Signature=IJ4GkUTlR0SLCDllId4tM52xe8o%3D&response-content-disposition=inline%3B%20filename%3DMillennials_Will_Benefit_and_Suffer_Due.pdf
   Google Scholar
• Azmak, O., Bayer, H., Caplin, A., Chun, M., Glimcher, P., Koonin, S., & Patrinos, A. (2015). Using Big data to understand the human condition: The Kavli HUMAN project. Big Data, 3(3), 173–188. doi:10.1089/big.2015.0012
   PubMed Web of Science ®Google Scholar
• Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. doi:10.1126/science.aaa1160
   PubMed Web of Science ®Google Scholar
• Bartsch, A., Vorderer, P., Mangold, R., & Viehoff, R. (2008). Appraisal of emotions in media use: Toward a process model of meta-emotion and emotion regulation. Media Psychology, 11(1), 7–27. doi:10.1080/15213260701813447
   Web of Science ®Google Scholar
• Battestini, A., Setlur, V., & Sohn, T. (2010, September). A large scale study of text-messaging use. Proceedings of the 12th international conference on human computer interaction with mobile devices and services, Lisbon, Portugal (pp. 229–238). ACM.
   Google Scholar
• Bawden, D., & Robinson, L. (2009). The dark side of information: Overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35(2), 180–191. doi:10.1177/0165551508095781
   Web of Science ®Google Scholar
• Beebe, B., Jaffe, J., & Lachmann, F. (2005). A dyadic systems view of communication. In J. Auerbach, K. Levy, C. Schaffer, & M. Stein (Eds.), Relatedness, self-definition and mental representation (pp. 23–42). London, United Kingdom: Routledge.
   Google Scholar
• Bellman, S., Wooley, B., & Varan, D. (2016). Program–Ad matching and television ad effectiveness: A reinquiry using facial tracking software. Journal of Advertising, 45(1), 72–77. doi:10.1080/00913367.2015.1085816
   Web of Science ®Google Scholar
• Berinsky, A. J., Quek, K., & Sances, M. (2012). Conducting online experiments on mechanical turk. Newsletter of the APSA Experimental Section, 3(1), 2–9.
   Google Scholar
• Bohannon, J. (2011). Social science for pennies. Science, 334(6054), 307. doi:10.1126/science.334.6054.307
   PubMed Web of Science ®Google Scholar
• Boker, S. M., Brick, T. R., Pritikin, J. N., Wang, Y., Oertzen, T. V., Brown, D., … Neale, M. C. (2015). Maintained individual data distributed likelihood estimation (middle). Multivariate Behavioral Research, 50(6), 706–720. doi:10.1080/00273171.2015.1094387
   PubMed Web of Science ®Google Scholar
• Boyd, D. (2015). Making sense of teen life: Strategies for capturing ethnographic data in a networked era. In E. Hargittai & C. Sandvig (Eds.), Digital research confidential: The secrets of studying behavior online (pp. 79–10). Cambridge, MA: MIT Press.
   Google Scholar
• Brasel, S. A., & Gips, J. (2011). Media multitasking behavior: Concurrent television and computer usage. Cyberpsychology, Behavior, and Social Networking, 14(9), 527–534. doi:10.1089/cyber.2010.0350
   PubMed Web of Science ®Google Scholar
• Breuel, T. M. (2008). The OCRopus open source OCR system. In Proc. SPIE 6815, document recognition and retrieval XV (Vol. 6815, pp. 68150F1–68150F15). Bellingham, WA: International Society for Photonics and Electronics.
   Google Scholar
• Brown, J. S. (2000). Growing up: Digital: How the web changes work, education, and the ways people learn. Change: the Magazine of Higher Learning, 32(2), 11–20. doi:10.1080/00091380009601719
   Google Scholar
• Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5. doi:10.1177/1745691610393980
   Web of Science ®Google Scholar
• Burgoon, J. K., Stern, L. A., & Dillman, L. (2007). Interpersonal adaptation: Dyadic interaction patterns. Cambridge, England: Cambridge University Press.
   Google Scholar
• Butler, D. (2007, October 10). Data sharing threatens privacy. Nature. Retrieved from https://www.nature.com/news/2007/071010/full/449644a.html
   Google Scholar
• Cacciatore, M. A., Scheufele, D. A., & Iyengar, S. (2016). The end of framing as we know it… and the future of media effects. Mass Communication and Society, 19(1), 7–23. doi:10.1080/15205436.2015.1068811
   Web of Science ®Google Scholar
• Canny, J. (1986). A computational approach to edge detection. IEEE Transaction on Pattern Analysis and Machine Intelligence, 8(6), 679–698. doi:10.1109/TPAMI.1986.4767851
   PubMed Web of Science ®Google Scholar
• Cappella, J. N. (2017). Vectors into the future of mass and interpersonal communication research: Big data, social media, and computational social science. Human Communication Research, 43(4), 545–558. doi:10.1111/hcre.12114
   PubMed Web of Science ®Google Scholar
• Carey, J. (1992). Communication as culture, revised edition: Essays on media and society (media and popular culture). New York, NY: Routledge.
   Google Scholar
• Carstensen, L. L., Isaacowitz, D. M., & Charles, S. T. (1999). Taking time seriously: A theory of socioemotional selectivity. American Psychologist, 54, 165–181. doi:10.1037/0003-066X.54.3.165
   PubMed Web of Science ®Google Scholar
• Charles, S. T., Piazza, J. R., Mogle, J., Sliwinski, M. J., & Almeida, D. M. (2013). The wear and tear of daily stressors on mental health. Psychological Science, 24(5), 733–741. doi:10.1177/0956797612462222
   PubMed Web of Science ®Google Scholar
• Charness, N., Fox, M. C., & Mitchum, A. L. (2011). Life-span cognition and information technology. In K. L. Fingerman, C. A. Berg, J. Smith, & T. C. Antonucci (Eds.), Handbook of life-span development (pp. 331–361). New York, NY: Springer.
   Google Scholar
• Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y., Chen, R., … Cheng, Y. (2012). Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell, 148(6), 1293–1307. doi:10.1016/j.cell.2012.02.009
   PubMed Web of Science ®Google Scholar
• Chiatti, A., Yang, X., Brinberg, M., Cho, M. J., Gagneja, A., Ram, N., … Giles, C. L. (2017). Text extraction from smartphone screenshots to archive in situ media behavior. Proceedings of the Ninth International Conference on Knowledge Capture, Austin, TX. ACM..
   Google Scholar
• Chittaranjan, G., Blom, J., & Gatica-Perez, D. (2013). Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, 17(3), 433–450. doi:10.1007/s00779-011-0490-1
   Web of Science ®Google Scholar
• Chon, J., & Cha, H. (2011). Lifemap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing, 10(2), 58–67. doi:10.1109/MPRV.2011.13
   Web of Science ®Google Scholar
• Cockburn, A., & McKenzie, B. (2001). What do web users do? An empirical analysis of web use. International Journal of Human-Computer Studies, 54(6), 903–922. doi:10.1006/ijhc.2001.0459
   Web of Science ®Google Scholar
• Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317–332. doi:10.1111/jcom.2014.64.issue-2
   Web of Science ®Google Scholar
• Conroy, M., Feezell, J. T., & Guerrero, M. (2012). Facebook and political engagement: A study of online political group membership and offline political engagement. Computers in Human Behavior, 28(5), 1535–1546. doi:10.1016/j.chb.2012.03.012
   Web of Science ®Google Scholar
• Correa, T., Hinsley, A. W., & de Zúñiga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253. doi:10.1016/j.chb.2009.09.003
   Web of Science ®Google Scholar
• Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society, 34, 187–220.
   Google Scholar
• Csikszentmihalyi, M., & Larson, R. (2014). Validity and reliability of the experience-sampling method. In Flow and the foundations of positive psychology (pp. 35–54). Dordrecht, Netherlands: Springer Netherlands.
   Google Scholar
• Culjak, I., Abram, D., Pribanic, T., Dzapo, H., & Cifrek, M. (2012, May). A brief introduction to OpenCV. MIPRO, 2012 Proceedings of the 35th international convention, Opatija, Croatia, 2012 (pp. 1725–1730). IEEE.
   Google Scholar
• Cutting, J. E., Brunick, K. L., & Candan, A. (2012). Perceiving event dynamics and parsing Hollywood films. Journal of Experimental Psychology: Human Perception and Performance, 38(6), 1476.
   PubMed Web of Science ®Google Scholar
• Datavyu Team (2014). Datavyu: A video coding tool [Computer software]. Retrieved from http://datavyu.org.
   Google Scholar
• DeStefano, D., & LeFevre, J. A. (2007). Cognitive load in hypertext reading: A review. Computers in Human Behavior, 23(3), 1616–1641. doi:10.1016/j.chb.2005.08.012
   Web of Science ®Google Scholar
• Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., … Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45), 15888–15893. doi:10.1073/pnas.1408439111
   PubMed Web of Science ®Google Scholar
• Dilliplane, S., Goldman, S. K., & Mutz, D. C. (2013). Televised exposure to politics: New measures for a fragmented media environment. American Journal of Political Science, 57(1), 236–248. doi:10.1111/j.1540-5907.2012.00600.x
   Web of Science ®Google Scholar
• Dingler, T., Agroudy, P. E., Matheis, G., & Schmidt, A. (2016, February). Reading-based screenshot summaries for supporting awareness of desktop activities. In Proceedings of the 7th Augmented Human International Conference 2016 (pp. 27). New York, NY: ACM.
   Google Scholar
• Do, T. M. T., & Gatica-Perez, D. (2014). The places of our lives: Visiting patterns and automatic labeling from longitudinal smartphone data. IEEE Transactions on Mobile Computing, 13(3), 638–648. doi:10.1109/TMC.2013.19
   Web of Science ®Google Scholar
• Dodge, M., & Kitchin, R. (2007). ‘Outlines of a world coming into existence’: Pervasive computing and the ethics of forgetting. Environment and Planning B: Planning and Design, 34(3), 431–445. doi:10.1068/b32041t
   Web of Science ®Google Scholar
• Dumais, S., Cutrell, E., Cadiz, J. J., Jancke, G., Sarin, R., & Robbins, D. C. (2016, January). Stuff I’ve seen: A system for personal information retrieval and re-use. In ACM SIGIR forum (Vol. 49, pp. No. 2, pp. 28–35). New York, NY: ACM.
   Google Scholar
• Dvir-Gvirsman, S., Tsfati, Y., & Menchen-Trevino, E. (2016). The extent and nature of ideological selective exposure online: Combining survey responses with actual web log data from the 2013 Israeli Elections. New Media & Society, 18(5), 857–877. doi:10.1177/1461444814549041
   Web of Science ®Google Scholar
• Eagle, N., Pentland, A. S., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 15274–15278. doi:10.1073/pnas.0900282106
   PubMed Web of Science ®Google Scholar
• Estes, W. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53, 134–140. doi:10.1037/h0045156
   PubMed Web of Science ®Google Scholar
• Eveland, W. P., Jr, Hutchens, M. J., & Shen, F. (2009). Exposure, attention, or “use” of news? Assessing aspects of the reliability and validity of a central concept in political communication research. Communication Methods and Measures, 3(4), 223–244. doi:10.1080/19312450903378925
   Google Scholar
• Fraley, R. C., & Hudson, N. W. (2014). Review of intensive longitudinal methods: An introduction to diary and experience sampling research. The Journal of Social Psychology, 154(1), 89–91. doi:10.1080/00224545.2013.831300
   Web of Science ®Google Scholar
• Geertz, C. (1998). Deep hanging out. The New York Review of Books, 45(16), 69–72.
   Google Scholar
• Gemmell, J., Bell, G., Lueder, R., Drucker, S., & Wong, C. (2002, December). MyLifeBits: Fulfilling the Memex vision. In Proceedings of the tenth ACM international conference on multimedia (pp. 235–238). New York, NY: ACM.
   Google Scholar
• Gentile, D. A., Li, D., Khoo, A., Prot, S., & Anderson, C. A. (2014). Mediators and moderators of long-term effects of violent video games on aggressive behavior: Practice, thinking, and action. JAMA Pediatrics, 168(5), 450–457. doi:10.1001/jamapediatrics.2014.63
   PubMed Web of Science ®Google Scholar
• GfK Research. (2017, January 23). Millennials account for nearly half of US cordless population. [Press Release]. Retrieved from http://www.gfk.com/en-us/insights/press-release/millennials-account-for-nearly-half-of-us-cordless-population-gfk-mri/
   Google Scholar
• Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051), 1878–1881. doi:10.1126/science.1202775
   PubMed Web of Science ®Google Scholar
• Gottlieb, G. 1996. Developmental psychobiological theory. In R. Cairns, G. Elder, & E. Costello (Eds.), Developmental science (Cambridge Studies in Social and Emotional Development, pp. 63–77). Cambridge: Cambridge University Press.
   Google Scholar
• Greitemeyer, T., & Mügge, D. O. (2014). Video games do affect social outcomes: A meta-analytic review of the effects of violent and prosocial video game play. Personality and Social Psychology Bulletin, 40(5), 578–589. doi:10.1177/0146167213520459
   PubMed Web of Science ®Google Scholar
• Gurrin, C., Smeaton, A. F., & Doherty, A. R. (2014). Lifelogging: Personal big data. Foundations and Trends® in Information Retrieval, 8(1), 1–125. doi:10.1561/1500000033
   Web of Science ®Google Scholar
• Harari, G. M., Müller, S. R., Mishra, V., Wang, R., Campbell, A. T., Rentfrow, P. J., & Gosling, S. D. (2017). An evaluation of students’ interest in and compliance with self-tracking methods: Recommendations for incentives based on three smartphone sensing studies. Social Psychological and Personality Science, 8(5), 479–492. doi:10.1177/1948550617712033
   Web of Science ®Google Scholar
• Hembrooke, H., & Gay, G. (2003). The laptop and the lecture: The effects of multitasking in learning environments. Journal of Computing in Higher Education, 15(1), 46–64. doi:10.1007/BF02940852
   Google Scholar
• Hermans, D., De Houwer, J., & Eelen, P. (2001). A time course analysis of the affective priming effect. Cognition & Emotion, 15(2), 143–165. doi:10.1080/02699930125768
   Web of Science ®Google Scholar
• Horton, J. J., Rand, D. G., & Zeckhauser, R. J. (2011). The online laboratory: Conducting experiments in a real labor market. Experimental Economics, 14(3), 399–425. doi:10.1007/s10683-011-9273-9
   Web of Science ®Google Scholar
• Jackson, L. A., Zhao, Y., Kolenic, A., III, Fitzgerald, H. E., Harold, R., & Von Eye, A. (2008). Race, gender, and information technology use: The new digital divide. CyberPsychology & Behavior, 11(4), 437–442. doi:10.1089/cpb.2007.0157
   PubMedGoogle Scholar
• Jacquemard, T., Novitzky, P., O’Brolcháin, F., Smeaton, A. F., & Gordijn, B. (2014). Challenges and opportunities of lifelog technologies: A literature review and critical analysis. Science and Engineering Ethics, 20(2), 379–409. doi:10.1007/s11948-013-9456-1
   PubMed Web of Science ®Google Scholar
• Jansen, B. J., & Spink, A. (2006). How are we searching the World Wide Web? A comparison of nine search engine transaction logs. Information Processing & Management, 42(1), 248–263. doi:10.1016/j.ipm.2004.10.007
   Web of Science ®Google Scholar
• Jenkins, H. (2006). Convergence culture: Where old and new media collide. New York, NY: NYU Press.
   Google Scholar
• Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103(1), 54. doi:10.1037/a0028347
   PubMed Web of Science ®Google Scholar
• Kahneman, D. (2011). Thinking, fast and slow. London, United Kingdom: Macmillan.
   Google Scholar
• Kaufman, D. R., Patel, V. L., Hilliman, C., Morin, P. C., Pevzner, J., Weinstock, R. S., … Starren, J. (2003). Usability in the real world: Assessing medical information technologies in patients’ homes. Journal of Biomedical Informatics, 36(1–2), 45–60.
   PubMed Web of Science ®Google Scholar
• Kaye, B. K., & Johnson, T. J. (2002). Online and in the know: Uses and gratifications of the web for political information. Journal of Broadcasting & Electronic Media, 46(1), 54–71. doi:10.1207/s15506878jobem4601_4
   Web of Science ®Google Scholar
• Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241–251. doi:10.1016/j.bushor.2011.01.005
   Web of Science ®Google Scholar
• Kim, Y., Chen, H. T., & de Zúñiga, H. G. (2013). Stumbling upon news on the Internet: Effects of incidental news exposure and relative entertainment use on political engagement. Computers in Human Behavior, 29(6), 2607–2614. doi:10.1016/j.chb.2013.06.005
   Web of Science ®Google Scholar
• Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences,111(24), 8788–8790.
   Google Scholar
• Krämer, N. C., & Winter, S. (2008). Impression management 2.0: The relationship of self-esteem, extraversion, self-efficacy, and self-presentation within social networking sites. Journal of Media Psychology, 20(3), 106–116. doi:10.1027/1864-1105.20.3.106
   Google Scholar
• Ksiazek, T. B., Malthouse, E. C., & Webster, J. G. (2010). News-seekers and avoiders: Exploring patterns of total news consumption across media and the relationship to civic participation. Journal of Broadcasting & Electronic Media, 54(4), 551–568. doi:10.1080/08838151.2010.519808
   Web of Science ®Google Scholar
• Kubey, R., & Csikszentmihalyi, M. (2002). Television addiction is no mere metaphor. Scientific American, 286(2), 74–80.
   PubMed Web of Science ®Google Scholar
• Kubey, R., & Csikszentmihalyi, M. (2013). Television and the quality of life: How viewing shapes everyday experience. New York, NY: Routledge.
   Google Scholar
• Kumar, R., & Tomkins, A. (2010, April). A characterization of online browsing behavior. In Proceedings of the 19th international conference on World Wide Web, Raleigh, NC (pp. 561–570). New York, NY: ACM.
   Google Scholar
• Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C., … Kim, D. J. (2013). Development and validation of a smartphone addiction scale (SAS). PloS One, 8(2), e56936. doi:10.1371/journal.pone.0056936
   PubMed Web of Science ®Google Scholar
• Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70. doi:10.1111/j.1460-2466.2000.tb02833.x
   Web of Science ®Google Scholar
• Lang, A., Sanders-Jackson, A., Wang, Z., & Rubenking, B. (2013). Motivated message processing: How motivational activation influences resource allocation, encoding, and storage of TV messages. Motivation and Emotion, 37(3), 508–517. doi:10.1007/s11031-012-9329-y
   Web of Science ®Google Scholar
• Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … Jebara, T. (2009). Life in the network: The coming age of computational social science. Science, 323(5915), 721. doi:10.1126/science.1167742
   PubMed Web of Science ®Google Scholar
• Lemke, J. L. (2000). Across the scales of time: Artifacts, activities, and meanings in ecosocial systems. Mind, Culture, and Activity, 7(4), 273–290. doi:10.1207/S15327884MCA0704_03
   Google Scholar
• Leong, L. (2017). Mobile Myanmar: The development of a mobile app culture in Yangon. Mobile Media & Communication, 5(2), 139–160. doi:10.1177/2050157917689950
   Web of Science ®Google Scholar
• Lienhart, R., & Maydt, J. (2002). An extended set of Haar-like features for rapid object detection. Proceedings of the 2002 IEEE international conference on image processing, Rochester, NY (Vol. 1, pp. 900–903). IEEE.
   Google Scholar
• Lin, Y. H., Lin, Y. C., Lee, Y. H., Lin, P. H., Lin, S. H., Chang, L. R., … Kuo, T. B. (2015). Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App). Journal of Psychiatric Research, 65, 139–145. doi:10.1016/j.jpsychires.2015.04.003
   PubMed Web of Science ®Google Scholar
• Lundby, K. (Ed.). (2014). Mediatization of communication (Vol. 21). Berlin, Germany: Walter de Gruyter GmbH & Co KG.
   Google Scholar
• Magnusson, D., & Cairns, R. B. (1996). Developmental science: Toward a unified framework. In R. B. Cairns, G. H. Elder, & E. J. Costello (Eds.), Developmental science (pp. 7–30). New York, NY: Cambridge University Press.
   Google Scholar
• Mark, G., Voida, S., & Cardello, A. (2012, May). A pace not dictated by electrons: An empirical study of work without email. Proceedings of the SIGCHI conference on human factors in computing systems, Austin, TX (pp. 555–564). ACM.
   Google Scholar
• Meaney, M. J., & Ferguson-Smith, A. C. (2010). Epigenetic regulation of the neural transcriptome: The meaning of the marks. Nature Neuroscience, 13(11), 1313. doi:10.1038/nn.2511
   PubMed Web of Science ®Google Scholar
• Mehl, M. R., & Conner, T. S. (Eds.). (2012). Handbook of research methods for studying daily life. New York, NY: Guilford Press.
   Google Scholar
• Meshi, D., Tamir, D. I., & Heekeren, H. R. (2015). The emerging neuroscience of social media. Trends in Cognitive Sciences, 19(12), 771–782. doi:10.1016/j.tics.2015.09.004
   PubMed Web of Science ®Google Scholar
• Min, J. K., Wiese, J., Hong, J. I., & Zimmerman, J. (2013, February). Mining smartphone data to classify life-facets of social relationships. In Proceedings of the 2013 conference on computer supported cooperative work, San Antonio, TX (pp. 285–294). New York, NY: ACM.
   Google Scholar
• Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2(4), 201–218.
   Google Scholar
• Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychology, 18, 112–117. doi:10.1111/j.1467-8721.2009.01619.x
   Web of Science ®Google Scholar
• Morgan, E. M., Snelson, C., & Elison-Bowers, P. (2010). Image and video disclosure of substance use on social media websites. Computers in Human Behavior, 26(6), 1405–1411. doi:10.1016/j.chb.2010.04.017
   Web of Science ®Google Scholar
• Morse, P. J., Neel, R., Todd, E., & Funder, D. (2015). Renovating situation taxonomies: Exploring the construction and content of fundamental motive situation types. Journal of Personality, 83(4), 389–403. doi:10.1111/jopy.12111
   PubMed Web of Science ®Google Scholar
• Murphy, J., Hofacker, C., & Mizerski, R. (2006). Primacy and recency effects on clicking behavior. Journal of Computer-Mediated Communication, 11(2), 522–535. doi:10.1111/jcmc.2006.11.issue-2
   Web of Science ®Google Scholar
• Nadkarni, A., & Hofmann, S. G. (2012). Why do people use Facebook? Personality and Individual Differences, 52(3), 243–249. doi:10.1016/j.paid.2011.11.007
   PubMed Web of Science ®Google Scholar
• Nass, C. I., & Reeves, B. (1991). Combining, distinguishing, and generating theories in communication: A domains of analysis framework. Communication Research, 18(2), 240–261. doi:10.1177/009365091018002006
   Web of Science ®Google Scholar
• Nesselroade, J. R. (1991). The warp and the woof of the developmental fabric. In R. M. Downs, L. S. Liben, & D. S. Palermo (Eds.), Visions of aesthetics, the environment and development: The legacy of Joachim F. Wohlwill (pp. 213–240). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Newell, A., & Card, S. K. (1985). The prospects for psychological science in human-computer interaction. Human-Computer Interaction, 1(3), 209–242. doi:10.1207/s15327051hci0103_1
   Google Scholar
• Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583–15587. doi:10.1073/pnas.0903620106
   PubMed Web of Science ®Google Scholar
• Otsu, N. (1979). A threshold selection method from gray-level histograms,”. IEEE Trans. Systems, Man, and Cybernetics, 9(1), 62–66. doi:10.1109/TSMC.1979.4310076
   Web of Science ®Google Scholar
• Pennebaker, J. W., Booth, R. J., Boyd, R. L., & Francis, M. E. (2015). Linguistic inquiry and word count: LIWC2015. Austin, TX: Pennebaker Conglomerates. www.LIWC.net
   Google Scholar
• Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H. J., Mößle, T., … Auriacombe, M. (2014). An international consensus for assessing internet gaming disorder using the new DSM‐5 approach. Addiction, 109(9), 1399–1406. doi:10.1111/add.12457
   PubMed Web of Science ®Google Scholar
• Quan-Haase, A., & Young, A. L. (2010). Uses and gratifications of social media: A comparison of Facebook and instant messaging. Bulletin of Science, Technology & Society, 30(5), 350–361. doi:10.1177/0270467610380009
   Google Scholar
• Raacke, J., & Bonds-Raacke, J. (2008). MySpace and Facebook: Applying the uses and gratifications theory to exploring friend-networking sites. Cyberpsychology & Behavior, 11(2), 169–174. doi:10.1089/cpb.2007.0056
   PubMedGoogle Scholar
• Ram, N., Brose, A., & Molenaar, P. C. M. (2013). Dynamic factor analysis: Modeling person-specific process. In T. Little (Ed.), Oxford handbook of quantitative methods volume 2 statistical analysis (pp. 441–457). New York, NY: Oxford University Press.
   Google Scholar
• Ram, N., & Diehl, M. (2015). Multiple time-scale design and analysis: Pushing towards real-time modeling of complex developmental processes. In M. Diehl, K. Hooker, & M. Sliwinski (Eds.), Handbook of intraindividual variability across the lifespan (pp. 308–323). New York, NY: Routledge.
   Google Scholar
• Ram, N., Brinberg, M., Pincus, A. L., & Conroy, D. E. (2017). The questionable ecological validity of ecological momentary assessment designs: Prospects for unobtrusive monitoring and person-specific analysis. Research in Human Development, 14(3), 253–270. doi:10.1080/15427609.2017.1340052
   PubMed Web of Science ®Google Scholar
• Ram, N., & Gerstorf, D. (2009). Time structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes. Psychology and Aging, 24(4), 778–791. doi:10.1037/a0017915
   PubMed Web of Science ®Google Scholar
• Rauthmann, J. F., Gallardo-Pujol, D., Guillaume, E. M., Todd, E., Nave, C. S., Sherman, R. A., … Funder, D. C. (2014). The situational eight DIAMONDS: A taxonomy of major dimensions of situation characteristics. Journal of Personality and Social Psychology, 107(4), 677. doi:10.1037/a0037250
   PubMed Web of Science ®Google Scholar
• Rauthmann, J. F., & Sherman, R. A. (2015). Ultra-brief measures for the situational eight DIAMONDS domains. European Journal of Psychological Assessment, 32(2), 165–174. doi:10.1027/1015-5759/a000245
   Web of Science ®Google Scholar
• Rauthmann, J. F., Sherman, R. A., & Funder, D. C. (2015). Principles of situation research: Towards a better understanding of psychological situations. European Journal of Personality, 29(3), 363–381. doi:10.1002/per.1994
   Web of Science ®Google Scholar
• Rawassizadeh, R., Tomitsch, M., Wac, K., & Tjoa, A. M. (2013). UbiqLog: A generic mobile phone-based life-log framework. Personal and Ubiquitous Computing, 17(4), 621–637. doi:10.1007/s00779-012-0511-8
   Web of Science ®Google Scholar
• Reeves, B. (1989). Theories about news and theories about cognition: Arguments for a more radical separation. American Behavioral Scientist, 33(2), 191–198. doi:10.1177/0002764289033002013
   Web of Science ®Google Scholar
• Reeves, B., Yeykelis, L., & Cummings, J. J. (2016). The use of media in media psychology. Media Psychology, 19(1), 49–71. doi:10.1080/15213269.2015.1030083
   Web of Science ®Google Scholar
• Richardson, I. E. G. (2003). H.264 and MPEG-4 video compression: Video coding for next-generation multimedia. Chichester, United Kingdom: John Wiley & Sons Ltd.
   Google Scholar
• Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu, M. M., & Gatford, M. (1995). Okapi at TREC-3. In D. K. Harmon (Ed.), Overview of the third text retrieval conference (Trec-3) (pp. 109–126). Darby, PA: DIANE Publishing Company.
   Google Scholar
• Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15(3), 351–357. doi:10.2307/2087176
   Web of Science ®Google Scholar
• Romantan, A., Hornik, R., Price, V., Cappella, J., & Viswanath, K. (2008). A comparative analysis of the performance of alternative measures of exposure. Communication Methods and Measures, 2(1–2), 80–99. doi:10.1080/19312450802062539
   Google Scholar
• Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948–958. doi:10.1016/j.chb.2012.12.001
   Web of Science ®Google Scholar
• Ross, L., & Nisbett, R. E. (2011). The person and the situation: Perspectives of social psychology. London, United Kingdom: Pinter & Martin Publishers.
   Google Scholar
• Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., … Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS One, 8(9), e73791. doi:10.1371/journal.pone.0073791
   PubMed Web of Science ®Google Scholar
• Seo, M. G., Goldfarb, B., & Barrett, L. F. (2010). Affect and the framing effect within individuals over time: Risk taking in a dynamic investment simulation. Academy of Management Journal, 53(2), 411–431. doi:10.5465/AMJ.2010.49389383
   PubMed Web of Science ®Google Scholar
• Sethna, J. P. (2006). Statistical mechanics: Entropy, order parameters, and complexity. Oxford: Oxford University Press.
   Google Scholar
• Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Big data, digital media, and computational social science: Possibilities and perils. The ANNALS of the American Academy of Political and Social Science, 659(1), 6–13. doi:10.1177/0002716215572084
   Web of Science ®Google Scholar
• Sherman, J. E., Spencer, J., Preisser, J. S., Gesler, W. M., & Arcury, T. A. (2005). A suite of methods for representing activity space in a healthcare accessibility study. International Journal of Health Geographics, 4(1), 24. doi:10.1186/1476-072X-4-24
   PubMedGoogle Scholar
• Smith, R. (2007, September). An overview of the Tesseract OCR engine. Proceedings of the ninth international conference on document analysis and recognition, Parana, Brazil (Vol. 2, pp. 629–633). IEEE.
   Google Scholar
• Su, N. M., Brdiczka, O., & Begole, B. (2013). The routineness of routines: Measuring rhythms of media interaction. Human–Computer Interaction, 28(4), 287–334. doi:10.1080/07370024.2012.697026
   Web of Science ®Google Scholar
• Sundar, S. (2009, February 12). Social psychology of interactivity in human-website interaction. In A. N. Joinson, K. Y. A. McKenna, T. Postmes, & U. Reips (Eds..), Oxford handbook of internet psychology. Oxford, United Kingdom: Oxford University Press. Retrieved from http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199561803.001.0001/oxfordhb-9780199561803-e-007
   Google Scholar
• Sundar, S. S., Bellur, S., Oh, J., Xu, Q., & Jia, H. (2014). User experience of on-screen interaction techniques: An experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping. Human–Computer Interaction, 29(2), 109–152. doi:10.1080/07370024.2013.789347
   Web of Science ®Google Scholar
• Sundar, S. S., Kalyanaraman, S., & Brown, J. (2003). Explicating web site interactivity: Impression formation effects in political campaign sites. Communication Research, 30(1), 30–59. doi:10.1177/0093650202239025
   Web of Science ®Google Scholar
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA (pp. 1–9). IEEE.
   Google Scholar
• Talukder, K. H., & Mallick, T. (2014). Connected component based approach for text extraction from color image. In Proceedings of the 17th IEEE International Conference on Computer and Information Technology, Helsinki, Finland (pp. 204–209). IEEE.
   Google Scholar
• Tamir, D. I., Zaki, J., & Mitchell, J. P. (2015). Informing others is associated with behavioral and neural signatures of value. Journal of Experimental Psychology: General, 144(6), 1114. doi:10.1037/xge0000122
   PubMed Web of Science ®Google Scholar
• Taneja, H., Webster, J. G., Malthouse, E. C., & Ksiazek, T. B. (2012). Media consumption across platforms: Identifying user-defined repertoires. New Media & Society, 14(6), 951–968. doi:10.1177/1461444811436146
   Web of Science ®Google Scholar
• Thelen, E., & Smith, L. B. (2006). Dynamic systems theories. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Thorson, E., Reeves, B., & Schleuder, J. (1985). Message complexity and attention to television. Communication Research, 12(4), 427–454. doi:10.1177/009365085012004001
   Web of Science ®Google Scholar
• Tossell, C., Kortum, P., Rahmati, A., Shepard, C., & Zhong, L. (2012, May). Characterizing web use on smartphones. In Proceedings of the SIGCHI conference on human factors in computing systems, Austin, TX (pp. 2769–2778). ACM.
   Google Scholar
• Turkle, S. (1994). Constructions and reconstructions of self in virtual reality: Playing in the MUDs. Mind, Culture, and Activity, 1(3), 158–167. doi:10.1080/10749039409524667
   Google Scholar
• Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458.
   PubMed Web of Science ®Google Scholar
• Valentino, N. A., Hutchings, V. L., Banks, A. J., & Davis, A. K. (2008). Is a worried citizen a good citizen? Emotions, political information seeking, and learning via the internet. Political Psychology, 29(2), 247–273. doi:10.1111/pops.2008.29.issue-2
   Web of Science ®Google Scholar
• Van Heck, G. L. (1984). The construction of a general taxonomy of situations. Personality Psychology in Europe: Theoretical and Empirical Developments, 1, 149–164.
   Google Scholar
• Vanderkam, D. (2017). localturk [Computer Software]. Retrieved from https://github.com/danvk/localturk
   Google Scholar
• Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, Kauai, Hawaii(Vol. 1, pp. I–I). IEEE.
   Google Scholar
• Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363–371. doi:10.1056/NEJMra1511480
   PubMed Web of Science ®Google Scholar
• Vorberg, D., Mattler, U., Heinecke, A., Schmidt, T., & Schwarzbach, J. (2003). Different time courses for visual perception and action priming. Proceedings of the National Academy of Sciences, 100(10), 6275–6280. doi:10.1073/pnas.0931489100
   PubMed Web of Science ®Google Scholar
• Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., … Campbell, A. T. (2014, September). StudentLife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, Seattle, WA (pp. 3–14). ACM.
   Google Scholar
• Wang, Z., & Lang, A. (2012). Reconceptualizing excitation transfer as motivational activation changes and a test of the television program context effects. Media Psychology, 15(1), 68–92. doi:10.1080/15213269.2011.649604
   Web of Science ®Google Scholar
• Wells, C., & Thorson, K. (2017). Combining big data and survey techniques to model effects of political content flows in Facebook. Social Science Computer Review, 35(1), 33–52. doi:10.1177/0894439315609528
   Web of Science ®Google Scholar
• West, R., White, R. W., & Horvitz, E. (2013, May). From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro (pp. 1399–1410). ACM.
   Google Scholar
• White, R. W., & Huang, J. (2010, July). Assessing the scenic route: Measuring the value of search trails in web logs. Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, Geneva, Switzerland (pp. 587–594). ACM.
   Google Scholar
• Yeykelis, L., Cummings, J. J., & Reeves, B. (2014). Multitasking on a single device: Arousal and the frequency, anticipation, and prediction of switching between media content on a computer. Journal of Communication, 64(1), 167–192. doi:10.1111/jcom.12070
   PubMed Web of Science ®Google Scholar
• Yeykelis, L., Cummings, J. J., & Reeves, B. (2018). The fragmentation of work, entertainment, E-Mail, and news on a personal computer: Motivational predictors of switching between media content. Media Psychology, 21(3), 377–402. doi:10.1080/15213269.2017.1406805
   Web of Science ®Google Scholar
• Yin, P., Ram, N., Lee, W. C., Tucker, C., Khandelwal, S., & Salathé, M. (2014, May). Two sides of a coin: Separating personal communication and public dissemination accounts in Twitter. In Pacific-Asia conference on knowledge discovery and data mining (pp. 163–175). Cham: Springer.
   Google Scholar
• Zacks, J. M., & Swallow, K. M. (2007). Event segmentation. Current Directions in Psychological Science, 16(2), 80–84. doi:10.1111/j.1467-8721.2007.00480.x
   PubMed Web of Science ®Google Scholar