by Anna Priante
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 social movement campaigns. This book comprises seven
chapters presenting research based on a multidisciplinary, mixed-method
approach, combining theories and methods from sociology, social
psychology, communication science, and computational social science. The
findings show that, with 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.
Anna’s defense was the 5000th PhD defense at the UT!
The role of Online Identity on Donations to Nonprofit Organizations in Online Health Campaigns
by Anna Priante, Ariana Need, Tijs van den Broek, and Djoerd Hiemstra
Nonprofit Organizations largely use social media to mobilize people for social causes and encourage participation in collective action, such as advocacy campaigns. However, little is known about the micro-level mechanisms that drive individual mobilization outcomes that require a substantial effort in participation such as collecting donations during advocacy campaigns. By answering the call to combine motivational and structural factors that explain the mechanisms driving people’s engagement in collective action via social media, we focus on the role of online social identity as a motivator to engage in campaigns, and on individual network positions as opportunity structures for online mobilization. Using the 2014 US Movember health movement campaign on Twitter as an empirical context, we adopt a multi-method approach combining Natural Language Processing, social network analysis and multivariate regression analysis to investigate the effects of online social identity and structural network position on the amount of collected donations for medical research during campaign. We find that only social identities related to occupations and professions have significant effects on the amount of collected donation, whereas network position matters when movement members are central in the communication process because they connect different cohesive subgroups, or communities of the network, characterized by the prevalence of weak ties. We show the importance of integrating the study of identity and network to advance our understanding of online micro-mobilization dynamics. This study offers contributions to research at the intersection of research on the non-profit sector, social movements, media and communication, and health fundraising.
To be presented at the 78th Annual Meeting of the Academy of Management on 14 August 2018 in Chicago, USA
How Online Identity influences Collected Donations in Online Health Campaigns
by Anna Priante, Michel Ehrenhard, Tijs van der Broek, Ariana Need, Djoerd Hiemstra
Health advocacy organizations increasingly use social media to engage people in fundraising campaigns for medical research, such as cancer prevention. However, little is known about the effectiveness of online health campaigns and the psychosocial mechanisms that drive people’s voluntary engagement to collect money for medical research. By using identity-based motivation theory from social psychology, we focus on campaign participants’ online occupational identity, such as being a doctor, and how it provides motivation to collect donations. We investigate the mechanisms, such as fundraisers’ Twitter activity as a cognitive process and their central network positions in online communication, that mediate the relationship between identity and donations.
We adopt a multi-method approach combining automatic text analysis, Natural Language Processing from computational linguistics, social network analysis and multivariate regression analysis. Using the 2014 US Movember health movement campaign on Twitter as an empirical context, we find that when people are engaged in health fundraising on Twitter, their success depends on the extent to which they act in occupational identity-congruent ways. In addition, we find that fundraisers’ Twitter activity as a sense-making, cognitive process – and not their central positions in online communication – mediates the relation between identity and donations.
We show the importance of integrating both people’s social identification and cognitive processes into theory and research for a better understanding of how occupational identity matters in online health campaigns. This study offers contributions to research at the intersection of health advocacy, social media use, and, more broadly, online social movements. We conclude by discussing the practical implications of these findings for health advocacy organizations.
To be presented at the 113th Annual Meeting of the American Sociological Association
(ASA 2018) on 11-14 August 2018 in Philadelphia, USA.
Classifying Social Identities Based on Twitter
by Anna Priante, Djoerd Hiemstra, Tijs van den Broek, Aaqib Saeed, Michel Ehrenhard, and Ariana Need
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 setting
To be presented at the EMNLP Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) on November 5 in Austin, Texas, USA.
Download the code book and classifier source code from github.
A Cross-Country Analysis of Movember
by Nugroho Dwi Prasetyo (TU Delft), Claudia Hauff (TU Delft), Dong Nguyen, Tijs van den Broek, Djoerd Hiemstra
Health campaigns that aim to raise awareness and subsequently raise funds for research and treatment are commonplace. While many local campaigns exist, very few attract the attention of a global audience. One of those global campaigns is Movember, an annual campaign during the month of November, that is directed at men's health with special focus on cancer and mental health. Health campaigns routinely use social media portals to capture people’s attention. Recently, researchers began to consider to what extent social media is effective in raising the awareness of health campaigns. In this paper we expand on those works by conducting an investigation across four different countries, while not only restricting ourselves to the impact on awareness but also on fund-raising. To that end, we analyze the 2013 Movember Twitter campaigns in Canada, Australia, the United Kingdom and the United States.
To be presented at the 6th International Workshop on Health Text Mining and Information Analysis (Louhi 2015) Workshop at EMNLP 2015 on September 17 in Lisbon, Portugal.
Composing a more complete and relevant Twitter dataset
by Han van der Veen
Social data is widely used by many researchers. Facebook, Twitter and other social networks are producing huge amounts of social data. This social data can be used for analyzing human behavior. Social datasets are typically created by a hashtag, however not all relevant data includes the hashtag. A better overview can be constructed with more data. This research is focusing on creating a more complete and relevant dataset. Using additional keywords for finding more relevant tweets and a filtering mechanism to filter out the irrelevant tweets. Three additional keywords methods are proposed and evaluated. One based on word frequency, one on probability of word in a dataset and the last method is using estimates about the volume of tweets. Two classifiers are used for filtering Tweets. A Naive Bayes classifier and a Support Vector Machine classifier are compared. Our method increases the size of the dataset with 105%. The average precision was reduced from 95% of only using a hashtag to 76% for a resulting dataset. These evaluations were executed on two TV-Shows and two sport events. A tool was developed that automatically executes all parts of the program. As input a specific hashtag of an event is required and using the hash will output a more complete and relevant dataset than using the original hashtag. This is useful for social researchers that uses Tweets, but also other researchers that uses Tweets as their data.
by Dong Nguyen, Tijs van den Broek, Claudia Hauff (TU Delft), Djoerd Hiemstra, and Michel Ehrenhard
We consider the task of automatically identifying participants’ motivations in the public health campaign Movember and investigate the impact of the different motivations on the amount of campaign donations raised. Our classification scheme is based on the Social Identity Model of Collective Action (van Zomeren et al., 2008). We find that automatic classification based on Movember profiles is fairly accurate, while automatic classification based on tweets is challenging. Using our classifier, we find a strong relation between types of motivations and donations. Our study is a first step towards scaling-up collective action research methods.
The paper will be presented at the Conference on Empirical Methods in Natural Language Processing (EMNLP) on September 17-21, in Lisbon, Portugal.
Determine the User Country of a Tweet
by Han van der Veen, Djoerd Hiemstra, Tijs van den Broek, Michel Ehrenhard, and Ariana Need
In the widely used message platform Twitter, about 2% of the tweets contains the geographical location through exact GPS coordinates (latitude and longitude). Knowing the location of a tweet is useful for many data analytics questions. This research is looking at the determination of a location for tweets that do not contain GPS coordinates. An accuracy of 82% was achieved using a Naive Bayes model trained on features such as the users' timezone, the user's language, and the parsed user location. The classiffier performs well on active Twitter countries such as the Netherlands and United Kingdom. An analysis of errors made by the classiffier shows that mistakes were made due to limited information and shared properties between countries such as shared timezone. A feature analysis was performed in order to see the effect of different features. The features timezone and parsed user location were the most informative features.
It is official! Twitter awards the University of Twente with a prestigious Twitter #DataGrant (with Tijs van den Broek, Michel Ehrenhard and Ariana Need). Twitter awarded 6 out 1,300 proposals.
Our research project aims to study the diffusion process and effectiveness of cancer early detection campaigns. We plan to analyse popular Twitter campaigns covering different types of cancer and geographical scopes, such as #Mamming (breast cancer), #Movember (prostate cancer), #DaveDay (pancreatic cancer) and #HPVReport (cervical cancer). We aim to map the diffusion process in detail by determining key events and actors that accelerate the diffusion process. Social network analysis will reveal if and when the campaign leads to word-of-mouth discussion, promotion and responses. We also aim to assess the effectiveness of the campaigns by comparing the frequency and sentiment of mentions of a particular type of cancer (e.g. breast cancer in case of #mamming) before and after the campaign.