Presentation Type
Presentation
Faculty Mentor’s Full Name
Libby Metcalf
Faculty Mentor’s Department
Parks and Recreation
Abstract / Artist's Statement
The purpose of this study is to evaluate how effectively conservation nonprofits communicate their values, or beliefs linked to emotional effect, to the public. This study compares the basic values embedded in mission, vision, and values statements (MVVs) to the values embedded in tweets. The similarity between MVVs and tweets will be compared based on organization size in order to identify any relationship between the size of a nonprofit and how well they communicate values. Finally, the values found in tweets and MVVs will be used to identify a current audience each nonprofit connects to most. The primary method of data collection will be text mining, which collects and stores text data for analysis. Using Python, web scraping will collect MVVs from each nonprofit’s website and the tweepy package will collect a large volume of tweets. To identify values, a list of synonyms for each of the 10 basic human values identified by Shalom Schwartz (2012) will be used to generate values frequencies lists for both a nonprofit’s MVVs and tweets. Values similarity will be measured using cosine similarity, a calculation that compares the two frequencies lists. Ideally, conservation nonprofits would communicate the same values in both their communication to the public using social media and what they claim to represent in their MVVs. Additionally, if a nonprofit is looking to have a broad support base, they should be using language that appeals to a variety of political audiences. The methods used in this study represent a novel application of text mining to conservation efforts and has broader implications for improving science communication with the public. This study is based on the knowledge that language is strongly connected to values, and communication has the ability to polarize audiences or unify them.
Category
Social Sciences
Conservation Nonprofits and Communication of Values
The purpose of this study is to evaluate how effectively conservation nonprofits communicate their values, or beliefs linked to emotional effect, to the public. This study compares the basic values embedded in mission, vision, and values statements (MVVs) to the values embedded in tweets. The similarity between MVVs and tweets will be compared based on organization size in order to identify any relationship between the size of a nonprofit and how well they communicate values. Finally, the values found in tweets and MVVs will be used to identify a current audience each nonprofit connects to most. The primary method of data collection will be text mining, which collects and stores text data for analysis. Using Python, web scraping will collect MVVs from each nonprofit’s website and the tweepy package will collect a large volume of tweets. To identify values, a list of synonyms for each of the 10 basic human values identified by Shalom Schwartz (2012) will be used to generate values frequencies lists for both a nonprofit’s MVVs and tweets. Values similarity will be measured using cosine similarity, a calculation that compares the two frequencies lists. Ideally, conservation nonprofits would communicate the same values in both their communication to the public using social media and what they claim to represent in their MVVs. Additionally, if a nonprofit is looking to have a broad support base, they should be using language that appeals to a variety of political audiences. The methods used in this study represent a novel application of text mining to conservation efforts and has broader implications for improving science communication with the public. This study is based on the knowledge that language is strongly connected to values, and communication has the ability to polarize audiences or unify them.