The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of opinion mining. We design and implement the POPmine system which is able to collect texts from web-based conventional media (news items in mainstream media sites) and social media (blogs and Twitter) and to process those texts, recognizing topics and political actors, analyzing relevant linguistic units, and generating indicators of both frequency of mention and polarity (positivity negativity) of mentions to political actors across sources, types of sources, and across time.
The majority of the work using social media data applied to political scenarios consist in ad hoc studies where researchers collect data from a given social network during a pre-defined period of time and produce their specific analysis or predictions. The availability of open source research platforms in this area is scarse. Usually researchers use specific APIs and software modules to produce their studies. One research platform is Trendminer @cite_12 , an open source platform for real time analysis of Twitter data. It is the most similar work to POPmine although it only focus on Twitter and does not include sentiment analysis module, while POPmine allows cross media data collection and analysis, including sentiment.
@cite_12
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Using social media for political discourse is increasingly becoming common practice, especially around election time. Arguably, one of the most interesting aspects of this trend is the possibility of ''pulsing'' the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations. Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute. Positive results have been reported in a few occasions, but without an analysis on what principle enables them. This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks. This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections. We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis. Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible. In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance. Second, we reveal some major challenges that limit the predictability of election results through data from social media. We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow.