The primary question guiding my research paper for this class is—What role do topic, petitioner gender and linguistic components play in successful e-petitions? Using a purposive sampling technique based on petition topic and petitioner gender, I initially chose two popular e-petitioning websites for data collection as both these platforms allow for easy access and extraction of successful petitions while explicitly displaying the petitioner’s self-reported gender.
However, heeding Professor Stromer-Galley’s recommendation of narrowing down the focus even more to be able to find sizeable differences between the petitions, I am now relying on single-source data but with higher number of cases for a qualitative content analysis. Nonetheless, I am undecided whether to include two separate topics or just one to even further increase the number of male- and female-authored petitions from the same website.
Website1— 2 male topic1, 2 female topic1, 2 male topic2, 2 female topic2
Website2—2 male topic1, 2 female topic1, 2 male topic2, 2 female topic2
Website1—4 male topic1, 4 female topic1, 4 male topic2, 4 female topic2
Website1—8 male topic1 & 8 female topic1
Why is it important to consider both the variables of gender and topic rather than just one—of gender? Quite simply, while using a straightforward nominal variable of gender and doing a univariate analysis would allow me to describe linguistic characteristics of successful petitions, having the variable of topic could allow for a bivariate analysis explaining the relationship between the two variables, in addition to the possibility of displaying greater linguistic variation.
Fink et al. (2011) describe sentiment analysis as a means to extract and analyze real-time positively- and negatively-valenced sentiments or attitudes of social media users as expressed through text-based artifacts such as messages, comments, blogs, and such. For the purpose of sentiment analysis, they used a convenience sample of explicitly valenced product and movie reviews. In terms of my research, petitions are mostly either negatively valenced when there is a grievance, or positively valenced when there is a request. But for a holistic view of implicit and explicit traits of successful petitions, I will also examine neutral elements (neither positive nor negative) by content analyzing the linguistic features. Another potential problem of doing a sentiment analysis instead of content analysis is drawing accurate inferences of attitudes or sentiments of petitioners. Lastly, while it is relatively easier to code straightforward sentiments such as anger or rage, how does one analyze more complex sentiments of humor expressed through sarcasm or wit?