During elections in emerging democracies, communication behavior can indicate the relative freedom of expression perceived by individuals and organizations. Communication is critical for citizens to stay informed and make sense of competing political visions, platforms, and candidates. In Fall 2014, three years after the Arab Spring uprising that originated in Tunisia and resulted in the overthrow of long-time dictator Ben Ali, Tunisian citizens went to the voting booth to elect members of parliament and the next president. These were the first regular presidential elections since the Tunisian Revolution of 2011 and the adoption of the Constitution in January 2014, and the first free and fair presidential elections since independence from French colonialism in 1956. To explore the level of political tolerance and freedom of expression in this emerging democracy, we examined the contents and metadata of tweets during the election period. We used computational techniques (e.g., natural language processing, topic modeling, data visualization, and social graphing) as well as manual inspection of tweets to identify the main topics of political discussion and related social interaction. Our findings show a lively and open expression of political opinions, candidate positions, and policy issues appearing during the period of the 2014 elections, suggesting an increasingly democratic society in Tunisia.
Social media collected over time using keywords, hashtags and accounts associated with a particular geographic community might reflect that community’s main events, topics of discussion, and social interactions. We are interested in evidence for the support of community involvement that the aggregated Web pages and social media might help to create. We collected and analyzed Twitter data related to a geographic area over a two-year period to identify and characterize relevant topics and social interactions, and to evaluate the support for community involvement that such Twitter use might indicate. This kind of data collection has built-in biases, of course, just as local print media or online newsgroups do. We analyzed our data using the open source tool NodeXL to identify topics and their changes over time, and to create social graphs based on retweets and @ mentions that suggest interactions around topics. Our findings show: 1) distinct topics; 2) large and small clusters of social interactions around a variety of topics; 3) patterns suggesting what are called ‘community clusters’ and ‘tight crowd’ types of conversations; and, 4) evidence that Twitter supports local community involvement among users. Modeling topics over time and displaying visualizations of social interactions around different topics in a community can offer insights into the important events and issues during a given period. Such visualizations also reveal hidden (or obscure) topics due to a smaller number of participants — whether government representatives, voluntary associations, or citizens. There is clear evidence that Twitter supports social interaction and informal discussion or exchange around local topics among users, thereby facilitating community involvement.
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