Reviewed By: Melissa Balok, Edward Pantoja, Marie Ingram, Chloe Noland, Emma Weinberg
Link to article: http://sgo.sagepub.com/content/5/2/2158244015586000
Introduction
In the world of Web 2.0, tagging behavior is emerging as an important area of qualitative research for studies interested in learning more about language, community, cultural identity, and much more. In this article, Recuero et al (2015) perform a qualitative study on the use of hashtags and tagging behavior on Twitter in a specific political and regional area: the June 2013 political protests in Brazil. By comparing the localized tweets to “a theoretical background of the use of Twitter and hashtags in protests and the functions of language” (Recuero et al, 2015), the study was able to manifest not only specific context-based trends in tagging behavior, but also identify larger trends in virtual communication, personal incentive, and emotional versus recruiting behaviors.
The article begins by giving a history of the political climate and state of Brazil at the time the hash-tagging sample was occurring. The authors explain how tweets at this time were used as both a mobilization tool by protesters, as well as a way for the community to keep abreast of real-time occurrences at the rallies. This personalization of politics is further exemplified through a discourse on the effects of social media on people’s social movements, personal lives, and documentation/spreading of information during critical times. Delving into the function of language, which can be broken up into six main sections, the authors applied these linguistic classifications to the conversational and organizational quality of tweeting. Core research questions included: what are the types and communicative functions of hashtags used during protests; how do co-occurrences of hashtags depict different meanings and functions; and what are the trends in hash-tagging behavior of users as events unfold over time?
Method
Dealing with an overwhelming content-base, Recuero et al (2015) methodically analyzed a large dataset of tweets. 2,321,249 tweets were analyzed between June 13-20 in 2013. These dates were chosen because they consisted of the most Twitter activity during the protest. To effectively create and organize a large dataset, 35 keywords were tracked and inputted into the open source software yourTwapperkeeper to archive tweets that contained keywords. Researchers then attempted to classify the meaning of hashtags and their co-occurrences. Answers to these questions helped to create a context around the function of hashtags and how different co-occurrences could depict different meanings.
In order to objectively analyze the large dataset, Recuero et al (2015) used a coding procedure to categorize hashtags. Jakobson’s (1960) model of six main language functions was used to categorize hashtags according to their linguistic and communicative purposes. From this basic foundation for classification, hashtags which were found together within the same tweet were also classified. Due to the overlap of functions in a single tweet, a hierarchy was needed to establish and identify the dominant function of each tweet. The criteria used to determine the dominant function was to ask, “What is the purpose of this message?” Co-occurrences were also used with the previous criteria in order to categorize tweets. Lastly, the 500 most retweeted tweets were analyzed using similar mechanisms to create a context around the quantitative analysis.
Findings/Conclusion
Classification of the hashtags within the dataset were thus painstakingly paired to each of Jakobson’s (1960) six language functions. Contextual hashtags that frequently related to geographical location – where the event was happening – were classified as “referential”. Hashtags which indicated user emotion, thought and opinion, including protesters’ demands, were labeled as “expressive/emotive”. “Conative” hashtags were those that urged action and served to motivate other protestors, and “metalingual” hashtags, which referred to the content of the tweet.
In regards to the co-occurrences, the authors found that the most prominent types of hashtags that occurred together were conative-conative, encouraging action and strengthening the message through emphasis. Conative-referential hashtags were also preeminent, combining the call to mobilize with a physical location. Referential-referential hashtags helped to spread contextual information. Other co-occurrences of hashtags functioned to mobilize through opinions/demands, to contextualize the tweet in entirety, or to “sign” the tweet. Re-tweeted tweets were also analyzed, finding that most re-tweets were focused on the live events of the protests as they unfolded.
Overall, the results demonstrated tagging behavior during the protests in Brazil to have several functions: to call others to action, to align and coordinate protesters, to share information including metadata regarding content, and to express and support opinions.
Questions and Further Research
It would be interesting to see studies conducted with the same classification of tweets and hashtags that this study created, but with examination of different protests in different countries. A comparison of the results with the protests in Brazil could make for a better qualitative understanding of how different countries use hashtags, in addition to furthering examination of tagging behavior across countries. Additional questions that come to mind: do Internet users use hashtags in the same way, regardless of language and country of origin? This could lead to bigger behavior studies regarding humans and Internet behavior in general and how humans adapt to technology. Alternately, is there evidence of similar tagging behavior in applications that allow more than 140 characters per post? Recuero et al (2015) briefly mention that the character limit on Twitter could potentially cause users to eliminate tags that are not as important. This leads one to ask, what information about the protests is missing from Twitter? Could additional information be found on alternative ICT platforms, such as Facebook?
References:
Jakobson, R. & Sebeok, T. (1960). Linguistics and poetics. Style in Language, p. 350-377. Cambridge, MA: MIT Press.
Recuero, R., Zago, G., Bastos, M.T., & Araujo, R. (2015). Hashtag functions in the protests across Brazil. Sage Open Journals, published 11 May 2015, doi: 10.1177/2158244015586000