Emojis have many functions that support reading. Most obviously, they convey semantic information and support reading comprehension (Lo, CyberPsychology & Behavior, 11[5], 595-597, 2008; Riordan, Computers in Human Behavior, 76, 75-86, 2017b). However, it is undetermined whether emojis recruit the same perceptual and cognitive processes for identification and integration during reading as do words. To investigate whether emojis are processed like words, we used eye tracking to examine the time course of semantic processing of emojis during reading. Materials consisted of sentences containing a target word (e.g., coffee in the sentence "My tall coffee is just the right temperature") when there was no emoji present and when there was a semantically congruent (i.e., synonymous) emoji (e.g., the cup of coffee emoji, ) or an incongruent emoji (e.g., the beer mug emoji, ) present at the end of the sentence. Similar to congruency effects with words, congruent emojis were fixated for shorter periods and were less likely to be refixated than were incongruent emojis. In addition, congruent emojis were more frequently skipped than incongruent emojis, which suggests that semantic aspects of emoji processing begin in the parafovea. Finally, the presence of an emoji, relative to its absence increased target-word skipping rates and reduced total time on target words. We discuss the implications of our findings for models of eye-movement control during reading.Keywords Emojis . Eye movements and reading . Reading models . Parafoveal processing Are emojis processed like words? As with words, a core function of emojis is to convey semantic information (Lo, 2008;Riordan, 2017b). Although emojis serve many different functions to support reading, it is unknown whether the same cognitive and perceptual processes that support the identification and integration of words during reading also extend to emojis. Given the growing popularity of emojis on the internet, social media (Novak, Smailović, Sluban, & Mozetič, 2015), and in text-based communication (Tigwell & Flatla, 2016; i.e., more than 3,000 emojis are available to date; Unicode 12.1; https:// unicode.org/emoji/charts/full-emoji-list.html), we aimed to determine whether the time course of semantic processing for emojis is similar to words. The time course of semantic processing provides empirical constraints for the development of models of eye movement control during reading (for reviews, see Rayner, 1998Rayner, , 2009. Considering the ubiquity * Eliza Barach

International Conference on Cross-Cultural Decision Making (CCDM), FL, JUL 27-31, 2016International audienceWe describe the emergence of an online community from naturally occurring social media data. Our method uses patterns of word choice in an online social platform to characterize how a community forms in response to adverse events such as a terrorist attack. Our focus is English Twitter messages after the Charlie Hebdo terrorist attack in Paris in January 2015). We examined the text to find lexical variation associated with measures of valence, arousal and concreteness. We also examine the patterns of language use of the most prolific twitter users (top 2 % by number of tweets) and the most frequent tweets in our collection (top 2 % by number of retweets). Differences between users and tweets based on frequency are revealing about how lexical variation in tweeting behavior reflects evolution of a community in reaction to crisis events on an international scale

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