Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions

Springer Science and Business Media LLC - Tập 50 - Trang 392-405 - 2017
David Rodrigues1,2, Marília Prada1, Rui Gaspar3, Margarida V. Garrido1, Diniz Lopes1
1Department of Social and Organizational Psychology, Instituto Universitário de Lisboa (ISCTE-IUL), CIS-IUL, Lisbon, Portugal
2Goldsmiths University of London, London, UK
3William James Center for Research, ISPA - Instituto Universitáriov, Lisbon, Portugal

Tóm tắt

The use of emoticons and emoji is increasingly popular across a variety of new platforms of online communication. They have also become popular as stimulus materials in scientific research. However, the assumption that emoji/emoticon users’ interpretations always correspond to the developers’/researchers’ intended meanings might be misleading. This article presents subjective norms of emoji and emoticons provided by everyday users. The Lisbon Emoji and Emoticon Database (LEED) comprises 238 stimuli: 85 emoticons and 153 emoji (collected from iOS, Android, Facebook, and Emojipedia). The sample included 505 Portuguese participants recruited online. Each participant evaluated a random subset of 20 stimuli for seven dimensions: aesthetic appeal, familiarity, visual complexity, concreteness, valence, arousal, and meaningfulness. Participants were additionally asked to attribute a meaning to each stimulus. The norms obtained include quantitative descriptive results (means, standard deviations, and confidence intervals) and a meaning analysis for each stimulus. We also examined the correlations between the dimensions and tested for differences between emoticons and emoji, as well as between the two major operating systems—Android and iOS. The LEED constitutes a readily available normative database (available at www.osf.io/nua4x ) with potential applications to different research domains.

Tài liệu tham khảo

Antheunis, M. L., Valkenburg, P. M., & Peter, J. (2007). Computer-mediated communication and interpersonal attraction: An experimental test of two explanatory hypotheses. Cyber Psychology and Behavior, 10, 831–836. doi:10.1089/cpb.2007.9945 Antheunis, M. L., Valkenburg, P. M., & Peter, J. (2010). Getting acquainted through social network sites: Testing a model of online uncertainty reduction and social attraction. Computers in Human Behavior, 26, 100–109. doi:10.1016/j.chb.2009.07.005 Beltrone, G. (2015). Everyone is an emoji in this bizarre and terrifying French McDonald’s ad. AdWeek. Retrieved from www.adweek.com/adfreak/everyone-emoji-bizarre-and-terrifying-french-mcdonalds-ad-166335 Blechert, J., Meule, A., Busch, N. A., & Ohla, K. (2014). Food-pics: An image database for experimental research on eating and appetite. Frontiers in Psychology, 5, 617. doi:10.3389/fpsyg.2014.00617 Bonin, P., Peereman, R., Malardier, N., Méot, A., & Chalard, M. (2003). A new set of 299 pictures for psycholinguistic studies: French norms for name agreement, image agreement, conceptual familiarity, visual complexity, image variability, age of acquisition, and naming latencies. Behavior Research Methods, Instruments, & Computers, 35, 158–167. doi:10.3758/BF03195507 Bradley, M. M., & Lang, P. J. (1999a). Affective Norms for English Words (ANEW): stimuli, instruction manual, and affective ratings (Technical Report C-1). Gainesville: University of Florida, Center for Research in Psychophysiology. Bradley, M. M., & Lang, P. J. (1999b). International Affective Digitized Sounds (IADS): Stimuli, instruction manual and affective ratings (Technical Report B-2). Gainesville: University of Florida, Center for Research in Psychophysiology. Burnap, P., Gibson, R., Sloan, L., Southern, R., & Williams, M. (2016). 140 characters to victory? using twitter to predict the UK 2015 general election. Electoral Studies, 41, 230–233. doi:10.1016/j.electstud.2015.11.017 Carvalho, P., Sarmento, L., Silva, M. J., & de Oliveira, E. (2009). Clues for detecting irony in user-generated contents: Oh…‼ It’s “so easy”; -). In Proceedings of the 1st international CIKM workshop on topic–sentiment analysis for mass opinion (pp. 53–56). New York: ACM Press. doi:10.1145/1651461.1651471 Chambers, C. T., & Craig, K. D. (1998). An intrusive impact of anchors in children’s faces pain scales. Pain, 78, 27–37. doi:10.1016/S0304-3959(98)00112-2 Charbonnier, L., van Meer, F., van der Laan, L. N., Viergever, M. A., & Smeets, P. A. M. (2016). Standardized food images: A photographing protocol and image database. Appetite, 96, 166–173. doi:10.1016/j.appet.2015.08.041 Comesaña, M., Soares, A. P., Perea, M., Piñeiro, A. P., Fraga, I., & Pinheiro, A. (2013). ERP correlates of masked affective priming with emoticons. Computers in Human Behavior, 29, 588–595. doi:10.1016/j.chb.2012.10.020 Dan-Glauser, E. S., & Scherer, K. R. (2011). The Geneva Affective Picture Database (GAPED): A new 730-picture database focusing on valence and normative significance. Behavior Research Methods, 43, 468. doi:10.3758/s13428-011-0064-1 Davidov, D., Tsur, O., & Rappoport, A. (2010). Enhanced sentiment learning using Twitter hashtags and smileys. In Proceedings of the 23rd international conference on computational linguistics (pp. 241–249). Stroudsburg: Association for Computational Linguistics. Retrieved from http://dl.acm.org/citation.cfm?id=1944566.1944594 Derks, D., Bos, A. E. R., & von Grumbkow, J. (2008). Emoticons and online message interpretation. Social Science Computer Review, 26, 379–388. doi:10.1177/0894439307311611 Dresner, E., & Herring, S. C. (2010). Functions of the nonverbal in CMC: Emoticons and illocutionary force. Communication Theory, 20, 249–268. doi:10.1111/j.1468-2885.2010.01362.x Ebner, N. C., Riediger, M., & Lindenberger, U. (2010). FACES—A database of facial expressions in young, middle-aged, and older women and men: Development and validation. Behavior Research Methods, 42, 351–362. doi:10.3758/BRM.42.1.351 Eysenbach, G. (2011). Infodemiology and infoveillance: Tracking online health information and cyberbehavior for public health. American Journal of Preventive Medicine, 40, S154–S158. doi:10.1016/j.amepre.2011.02.006 Fane, J., MacDougall, C., Jovanovic, J., Redmond, G., & Gibbs, L. (2016). Exploring the use of emoji as a visual research method for eliciting young children’s voices in childhood research. Early Child Development and Care. doi:10.1080/03004430.2016.1219730 Fullwood, C., Orchard, L. J., & Floyd, S. A. (2013). Emoticon convergence in internet chat rooms. Social Semiotics, 23, 648–662. doi:10.1080/10350330.2012.739000 Ganster, T., Eimler, S. C., & Krämer, N. C. (2012). Same same but different!? the differential influence of smilies and emoticons on person perception. Cyber Psychology, Behavior, and Social Networking, 15, 226–230. doi:10.1089/cyber.2011.0179 Garcia-Marques, T., Mackie, D. M., Claypool, H. M., & Garcia-Marques, L. (2004). Positivity can cue familiarity. Personality and Social Psychology Bulletin, 30, 585–593. doi:10.1177/0146167203262856 Garrido, M. V., Lopes, D., Prada, M., Rodrigues, D., Jerónimo, R., & Mourão, R. P. (2016). The many faces of a face: Comparing stills and videos of facial expressions in eight dimensions (SAVE database). Behavior Research Methods. doi:10.3758/s13428-016-0790-5 Gaspar, R., Barnett, J., & Seibt, B. (2015). Crisis as seen by the individual: The norm deviation approach. Psyecology, 6, 103–135. doi:10.1080/21711976.2014.1002205 Gaspar, R., Pedro, C., Panagiotopoulos, P., & Seibt, B. (2016). Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events. Computers in Human Behavior, 56, 179–191. doi:10.1016/j.chb.2015.11.040 Gülşen, T. T. (2016). You tell me in emojis. In O. Ogata & T. Akimoto (Eds.), Computational and cognitive approaches to narratology (pp. 354–375). Hershey: Information Science Reference. Han, D. H., Yoo, H. J., Kim, B. N., McMahon, W., & Renshaw, P. F. (2014). Brain activity of adolescents with high functioning autism in response to emotional words and facial emoticons. PLoS ONE, 9, 1–8. doi:10.1371/journal.pone.0091214 Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., & Kaymak, U. (2013). Exploiting emoticons in sentiment analysis. In Proceedings of the 28th annual ACM symposium on applied computing (pp. 703–710). New York: ACM Press. doi:10.1145/2480362.2480498 Huang, A. H., Yen, D. C., & Zhang, X. (2008). Exploring the potential effects of emoticons. Information Management, 45, 466–473. doi:10.1016/j.im.2008.07.001 Jaeger, S. R., Vidal, L., Kam, K., & Ares, G. (2017). Can emoji be used as a direct method to measure emotional associations to food names? Preliminary investigations with consumers in USA and China. Food Quality and Preference, 56, 38–48. doi:10.1016/j.foodqual.2016.09.005 Kaye, L. K., Wall, H. J., & Malone, S. A. (2016). “Turn that frown upside-down”: A contextual account of emoticon usage on different virtual platforms. Computers in Human Behavior, 60, 463–467. doi:10.1016/j.chb.2016.02.088 Kerkhof, I., Goesaert, E., Dirikx, T., Vansteenwegen, D., Baeyens, F., D’Hooge, R., & Hermans, D. (2009). Assessing valence indirectly and online. Cognition and Emotion, 23, 1615–1629. doi:10.1080/02699930802469239 Krohn, F. B. (2004). A generational approach to using emoticons as nonverbal communication. Journal of Technical Writing and Communication, 34, 321–328. doi:10.2190/9eqh-de81-cwg1-qll9 Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual (Technical Report A-8). Gainesville: University of Florida. Lea, M., & Spears, R. (1992). Paralanguage and social perception in computer‐mediated communication. Journal of Organizational Communication, 2, 321–341. doi:10.1080/10919399209540190 Liu, B. (2012). Sentiment analysis and opinion mining. San Rafael: Morgan & Claypool. Liu, K.-L., Li, W.-J., & Guo, M. (2012). Emoticon smoothed language models for twitter sentiment analysis. In Proceedings of the 26th AAAI conference on artificial intelligence (pp. 1678–1684). New York: AAAI Press. Ljubešić, N., & Fišer, D. (2016). A global analysis of emoji usage. In proceedings of the 10th Web as corpus workshop (WAC-X) and the EmpiriST shared task (pp. 82–89). Stroudsburg: Association for Computational Linguistics. Lo, S.-K. (2008). The nonverbal communication functions of emoticons in computer-mediated communication. Cyber Psychology and Behavior, 11, 595–597. doi:10.1089/cpb.2007.0132 McDougall, S. J. P., Curry, M. B., & de Bruijn, O. (1999). Measuring symbol and icon characteristics: Norms for concreteness, complexity, meaningfulness, familiarity, and semantic distance for 239 symbols. Behavior Research Methods, Instruments, & Computers, 31, 487–519. doi:10.3758/BF03200730 Mendonça, R., Garrido, M. V., & Semin, G. R. (2016). A standardized database for Portuguese faces looking towards left, right and the front: The LRF Face Corpus. Manuscript submitted for publication. Milinovich, G. J., Williams, G. M., Clements, A. C. A., & Hu, W. (2014). Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infectious Diseases, 14, 160–168. doi:10.1016/S1473-3099(13)70244-5 Miller, H., Thebault-Spieker, J., Chang, S., Johnson, I., Terveen, L., & Hecht, B. (2016). “Blissfully happy” or “ready to fight”: varying interpretations of emoji. In International Conference on Web and Social Media (ICWSM) ’16 (pp. 259–268). New York: AAAI Press. Moore, A., Steiner, C. M., & Conlan, O. (2013). Design and development of an empirical smiley-based affective instrument. In 1st workshop on emotions and personality in personalized services (pp. 24–30). Rome: UMAP. Neff, J. (2015). Dove launches curly-haired emojis to end straight-hair dominance. Advertising Age. Retrieved from http://adage.com/article/digital/dove-launches-curly-haired-emojis-address-void/301203/ Negishi, M. (2014). Meet Shigetaka Kurita, the father of emoji. Wall Street Journal. Retrieved from http://blogs.wsj.com/japanrealtime/2014/03/26/meet-shigetaka-kurita-the-father-of-emoji/ Nelson, R. A., Tossell, C. C., & Kortum, P. (2015). Emoticon use in mobile communications :-). In Z. Yan (Ed.), Encyclopedia of mobile phone behavior (pp. 1–11). Hershey: IGI Global. Novak, P. K., Smailović, J., Sluban, B., & Mozetič, I. (2015). Sentiment of emojis. PLoS ONE, 10, e0144296. doi:10.1371/journal.pone.0144296 Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1–135. doi:10.1561/1500000011 Park, J., Baek, Y. M., & Cha, M. (2014). Cross-cultural comparison of nonverbal cues in emoticons on twitter: Evidence from big data analysis. Journal of Communication, 64, 333–354. doi:10.1111/jcom.12086 Paul, M. J., & Dredze, M. (2011). You are what you tweet: analyzing twitter for public health. In Fifth international AAAI conference on weblogs and social media (pp. 265–272). New York: AAAI Press. Pavalanathan, U., & Eisenstein, J. (2015). Emoticons vs. emojis on Twitter: A causal inference approach. arXiv:1510.08480 [cs.CL]. PEW Research Center. (2016). Smartphone ownership and Internet usage continues to climb in emerging economies. Retrieved from www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/ Prada, M., Rodrigues, D., Silva, R. R., & Garrido, M. V. (2015). Lisbon Symbol Database (LSD): Subjective norms for 600 symbols. Behavior Research Methods, 48, 1370–1382. doi:10.3758/s13428-015-0643-7 Proctor, R. W., & Vu, K.-P. L. (1999). Index of norms and ratings published in the psychonomic society journals. Behavior Research Methods, Instruments, & Computers, 31, 659–667. doi:10.3758/BF03200742 Richmond, V., & McCroskey, J. (2009). Human communication theory and research: Traditions and models. In D. Stacks & M. Salwen (Eds.), An integrated approach to communication theory and research (2nd ed., pp. 223–231). New York: Routledge. Siegel, R. M., Anneken, A., Duffy, C., Simmons, K., Hudgens, M., Lockhart, M. K., & Shelly, J. (2015). Emoticon use increases plain milk and vegetable purchase in a school cafeteria without adversely affecting total milk purchase. Clinical Therapeutics, 37, 1938–1943. doi:10.1016/j.clinthera.2015.07.016 Skiba, D. J. (2016). Face with tears of joy is word of the year: Are emoji a sign of things to come in health care? Nursing Education Perspectives, 37, 56–57. doi:10.1097/01.NEP.0000476112.24899.a1 Skovholt, K., Grønning, A., & Kankaanranta, A. (2014). The communicative functions of emoticons in workplace e-mails: :-). Journal of Computer-Mediated Communication, 19, 780–797. doi:10.1111/jcc4.12063 Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260 pictures: Norms for name agreement, image agreement, familiarity, and visual complexity. Journal of Experimental Psychology: Human Learning and Memory, 6, 174–215. doi:10.1037/0278-7393.6.2.174 Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61, 2544–2558. doi:10.1002/asi.21416 Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63, 163–173. doi:10.1002/asi.21662 Thompson, D., & Filik, R. (2016). Sarcasm in written communication: Emoticons are efficient markers of intention. Journal of Computer-Mediated Communication, 21, 105–120. doi:10.1111/jcc4.12156 Tossell, C. C., Kortum, P., Shepard, C., Barg-Walkow, L. H., Rahmati, A., & Zhong, L. (2012). A longitudinal study of emoticon use in text messaging from smartphones. Computers in Human Behavior, 28, 659–663. doi:10.1016/j.chb.2011.11.012 Tung, F.-W., & Deng, Y.-S. (2007). Increasing social presence of social actors in e-learning environments: Effects of dynamic and static emoticons on children. Displays, 28, 174–180. doi:10.1016/j.displa.2007.06.005 Vaiman, M., Wagner, M. A., Caicedo, E., & Pereno, G. L. (2017). Development and validation of an argentine set of facial expressions of emotion. Cognition and Emotion, 31, 249–260. doi:10.1080/02699931.2015.1098590 Vashisht, G., & Thakur, S. (2014). Facebook as a corpus for emoticons-based sentiment analysis. International Journal of Emerging Technology and Advanced Engineering, 4, 904–908. Vidal, L., Ares, G., & Jaeger, S. R. (2016). Use of emoticon and emoji in tweets for food-related emotional expression. Food Quality and Preference, 49, 119–128. doi:10.1016/j.foodqual.2015.12.002 Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43. doi:10.1177/009365096023001001 Walther, J. B., & D’Addario, K. P. (2001). The impacts of emoticons on message interpretation in computer-mediated communication. Social Science Computer Review, 19, 324. doi:10.1177/089443930101900307 Wang, H., & Castanon, J. A. (2015). Sentiment expression via emoticons on social media. Retrieved from arXiv:1511.02556 Wang, W., Zhao, Y., Qiu, L., & Zhu, Y. (2014). Effects of emoticons on the acceptance of negative feedback in computer-mediated communication. Journal of the Association for Information Systems, 15(8), 3. Retrieved from http://aisel.aisnet.org/jais/vol15/iss8/3 Wiebe, J., Wilson, T., & Cardie, C. (2005). Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39, 165–210. doi:10.1007/s10579-005-7880-9 Wohl, J. (2016, August 16). How marketers can win the great emoji arms race. Advertising Age. Retrieved from http://adage.com/article/digital/marketers-emoji-arms-race/303361/ Wolff, J. S., & Wogalter, M. S. (1998). Comprehension of pictorial symbols: Effects of context and test method. Human Factors, 40, 173–186. doi:10.1518/001872098779480433 Yuasa, M., Saito, K., & Mukawa, N. (2011). Brain activity associated with graphic emoticons. The effect of abstract faces in communication over a computer network. Electrical Engineering in Japan, 177, 36–45. doi:10.1002/eej.21162