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In online peer-to-peer fundraising, individual fundraisers, acting on behalf of nonprofit organizations, mobilize their social networks using social media to request donations. Whereas existing studies focus on networks of donors to explain success, we examine the role of the networks of fundraisers and their effect on fundraising outcomes. By drawing on social capital and network theories, we investigate how social capital derived from social media networks and fundraising groups explains individual fundraising success. Using the Movember health campaign on Twitter as an empirical context, we find that fundraising success is associated with a moderate level of centrality in social media networks and moderate group network size. In addition, we find that fundraisers interact only marginally on social media but prefer to connect with each other outside these platforms and engage in group fundraising. Our article contributes to research on fundraising and social networks and provides recommendations for practice.

Social movement organizations widely use social media to organize collective action for social change, such as cancer awareness campaigns. However, little is known about how effective online social movement campaigns are at generating social change by translating online action into meaningful (offline) action. This dissertation examines the micro-mobilization dynamics at play that can explain the effectiveness of online social movement campaigns. This book comprises seven chapters presenting research based on a multidisciplinary, mixed-meethod approach combining theories and methods from sociology, social psychology, communication science, and computational social science. The findings show that, by looking at the micro-mobilization dynamics of collective action, we can gain an important understanding of the mechanisms at work during online social movement campaigns and of the effectiveness of such campaigns in fostering communication processes related to the cause, obtaining important resources for the cause, developing a collective identity, and raising awareness.

Social movement research has long been part of studying how framing processes and emotional involvement influence participation in collective action. However, little is known about how these micro-level mechanisms affect individual mobilization outcomes in online social movement campaigns. In this study, I first explore how social movement organizations’ framing, through the use of language, symbols, and slogans, characterizes movement members’ discourse in social media and the extent to which the movement’s dominant framing becomes hegemonic during online mobilization. Then, I investigate how movement members’ adoption of the movement’s dominant framing in the online discourse and their level of emotional involvement in the framing process influence individual fundraising outcomes during a health-related campaign on Twitter. By combining automated text analysis, the use of a plagiarism detector, network visualizations, and regression analysis, I find that almost one-third of the discourse that movement members generated on Twitter during the campaign aligns with the Movember Foundation’s dominant framing. However, the more movement members use the movement’s language, slogans, and frames in their tweets, the less they collected in donations. By contrast, the use of emotional language in framing processes was positively associated with the amount collected in donations. In this way, I show the importance of the authenticity of communication and emotions as important micro-mobilization dynamics related to individual participation in online social movement campaigns. This study contributes to research on social movements, organizations, media, and communication.

We combine social theory and NLP methods to classify English-speaking Twitter users’ online social identity in profile descriptions. We conduct two text classification experiments. In Experiment 1 we use a 5-category online social identity classification based on identity and self-categorization theories. While we are able to automatically classify two identity categories (Relational and Occupational), automatic classification of the other three identities (Political, Ethnic/religious and Stigmatized) is challenging. In Experiment 2 we test a merger of such identities based on theoretical arguments. We find that by combining these identities we can improve the predictive performance of the classifiers in the experiment. Our study shows how social theory can be used to guide NLP methods, and how such methods provide input to revisit traditional social theory that is strongly consolidated in offline settings.