Date of Award
Doctor of Philosophy (PhD)
This dissertation examines the effectiveness of nonprofit advocacy on social media. With nationwide data on homelessness nonprofits in the United States, this is the first to examine how the such organizations use social media, what they frequently say on social media, and how effectively they use social media in order to garner public attention. Extending Guo and Saxton's Social Media Advocacy model, I propose a comprehensive model containing three major categories that explain the level of public attention. The first category is network characteristics, which includes network size and network influence. The second category is communication strategy, which contains three subcomponents of timing and pacing, targeting, and connecting strategy. The third category is content strategy with its two elements of content richness and sentiment/tone.
Nationwide data on homelessness nonprofits in the U.S. are compiled by combining multiple data sources; 326,620 Twitter messages sent by the sample organizations are collected via the Twitter API. Data analysis consists of three phases. Phase one presents findings on the national description of nonprofit organizations in the homelessness sector and their social media adoption and use. In phase two, a series of content analyses is conducted on the Twitter messages sent by homelessness nonprofits to explore topics discussed by the organizations. The findings from the topic modeling via LDA identify seven themes that are most frequently employed by homelessness nonprofits while successfully obtaining attention from other users. The seven themes include seeking support, homeless youth, housing and care service, domestic violence, emotional dialogue, homelessness, and veterans. In phase three, the study’s hypotheses are tested both at the organizational and message levels. The analysis generates the following major findings: network size, connecting strategies, informative content, and positive tone are found to be important determinants of the attention on social media both at the organizational level and message level. There may be different attention mechanisms between the organizational level and message level as some factors (e.g., public reply) are found to have a significant but different direction of relationship with attention between the two levels.
This study adds to the literature on social media advocacy by focusing on attention. The study applies Big Data approach to identify topics discussed by homelessness nonprofits, adds new factors of message strategy on “what to speak” and “how to speak”, and examines the determinants of audience attention at both the organizational and message levels. The findings from this study provides critical insights for nonprofit practitioners and advocates. In order to capture public attention, nonprofit organizations should spur efforts to increase their network size on social media, speak frequently, connect with others, offer informative and image content, and speak positively with an informal tone. Another important insight for nonprofit organizations is that how much attention an organization acquires on social media depends less on the organization’s resources, but more on effective use of social media. That is, no matter how small, an organization can increase awareness and drive audience attention by using social media strategically.
As homelessness nonprofits increasingly turn to social media to advocate for their constituents and homelessness issues, it is vital for nonprofit practitioners and advocates to employ effective social media strategies that make better use of their limited resources. This study will help build an evidence base for successful social media strategies, thus helping organizations influence public policy-makers, increase efforts to support their constituents, and allocate more resources to social media advocacy work.
An, Seongho, "Attention Strategies For Nonprofit Advocacy On Social Media: Results From A National Study Of Homelessness Nonprofits In The United States" (2019). Publicly Accessible Penn Dissertations. 3256.