Microblogging has become increasingly popular for commenting on current events, spreading gossip, and encouraging individualism--which favors its low-context communication channel. These social media (SM) platforms allow users to express opinions while interacting with a wide range of populations. Hashtags allow immediate identification of like-minded individuals worldwide on a vast array of topics. The output of the analytic tool, Linguistic Inquiry and Word Count (LIWC)--a program that associates psychological meaning with the frequency of use of specific words--may suggest the nature of individuals’ internal states and general sentiments. When applied to groupings of SM posts unified by a hashtag, such information can be helpful to community leaders during periods in which the forming of public opinion happens in parallel with the unfolding of political, economic, or social events. This is especially true when outcomes stand to impact the well-being of the group. Here, we applied the online tools, Google Translate and the University of Texas’s LIWC, to a 90-posting sample from a corpus of Colombian Spanish microblogs. On translated disjoint sets, identified by hashtag as being authored by advocates of voting “No,” advocates voting “Yes,” and entities refraining from hashtag use, we observed the value of LIWC’s Tone feature as distinguishing among the categories and the word “peace,” as carrying particular significance, due to its frequency of use in the data.
 Liang, Po-Wei and Bi-Ru Dai. 2013. Opinion Mining on Social Media Data. In Proceedings of IEEE 14th International Conference on Mobile Data Management.
 Nigam, Aastha, Henry K Dambanemuya, Madhav Joshi, and Nitesh Chawla. 2017. Harvesting Social Signals to Inform Peace Process Implementation and Monitoring. Big Data 5:4:1-19.
 Pennebaker, James W. and Laura A. King. 1999. “Linguistic Styles: Language Use as an Individual Difference.” Journal of Personality and Social Psychology 77:6:1296-1312.
 Garcia-Gavilanes, Ruth, Damele Quercia, and Alejandro Jaimes. 2013. Cultural Dimensions in Twitter: Time, Individualism and Power. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media.
 Pennebaker, J.W., R.L. Boyd, K. Jordan, & K. Blackburn. 2015. Development & psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin.
 Pennebaker, J.W. and Y.R. Tausczik. 2010. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology 29:1:24–54.
 Kacewicz, E., J. Pennebaker, M. Davis, M. Jeon, and A.C. Graesser. 2014. Pronoun Use Reflects Standings in Social Hierarchies. Journal of Language and Social Psychology, 33:2:125-143. https://doi.org/10.1177/0261927X13502654
 Cohn, M.A., M.R. Mehl, and J.W. Pennebaker. 2004. Linguistic Markers of Psychological Change Surrounding September 11, 2001. Psychological Science, 15:687-693. https://doi.org/10.1111/j.0956-7976.2004.00741.x
 Newman, M.L., J.W. Pennebaker, D.S. Berry, and J.M. Richards. 2003. Lying Words: Predicting Deception from Linguistic Styles. Personality and Social Psychology Bulletin, 29:5: 665–675. https://doi.org/10.1177/0146167203029005010