Master’s Thesis
Connective action: why people engage in activism on Facebook and Twitter
Master’s Programme in Communication Science Graduate School of Communication
University of Amsterdam
Name: Iris van Eijck (10759883) Supervisor: Dhr. dr. A.C. Goldberg Date: June 25, 2020
Abstract
Social media activism is on the rise since the internet has made it possible to interact and connect with millions of other people around the world. Therefore, it has become the core of activism in the digital age. What motivates people to engage in social media activism is important to explore, as social media facilitate the rapid diffusion of information on socio-political issues. Research has not yet elaborately focused on why people engage in social media activism and how it manifests itself when looking at different social media platforms. This study looks at the drivers of social media activism for Facebook and Twitter separately. It focuses on two types of motivation and three types of efficacy in regards to online activism. Using an online survey amongst Dutch youth (N = 190), the results suggest an important role for self-efficacy and social media efficacy. When high levels of self-efficacy and social media efficacy are experienced, young people tend to engage more in social media activism. The findings further demonstrate that there are differences between Twitter and Facebook, when looking at effect sizes. However, these findings are paired with a high level of uncertainty.
Introduction
Social media have become central venues for collective action (Valenzuela, 2013). They are key media for the coordination of political actions, the expression of political opinions and advocacy activities (Velasquez & LaRose, 2015a). Furthermore, they allow people to join political causes, to exchange political opinions and to receive mobilization information (Bennet & Segerberg, 2012; Yamamoto, Kushin, & Dalisay, 2017).
With the growth of social media political participation has taken new formations, especially when it comes to online activism (De Zúñiga, Copeland, & Bimber, 2014). Traditional political participation often recalls offline political activities, such as signing a petition and protesting on the streets, whereas social media has created new digital forms of political participation, such as changing your Facebook profile picture for a cause or posting an activist hashtag (Visser & Stolle, 2014). These activities on social media are criticized as ‘slacktivism’, because they are seen as low-cost and too simple to be called political
participation (Gladwell, 2010; Morozov, 2009). It is an ongoing debate amongst scholars whether the internet and social media have revolutionized political participation or traditional political participation is at danger (Vitak et al., 2011).
In the last decade, the relationship between online and offline political participation and social media use has been widely researched (Boulianne, 2015; Skoric, Zhu, Goh & Pang, 2016). However, there is a lack of research on the motivations driving online political
participation (Lilleker & Koc-Michalska, 2017). This study focuses on the activist dimension of political participation applied to the social media realm. Exploring motivations for social media activism is important, as social media have facilitated activism in the digital age. They have the power to motivate individuals to exchange similar ideas and take action together (Carty, 2018). For this reason, this study focuses on the motivations driving social media activism. Social media activism is defined as the participation of individuals through
communicative actions on social media to collectively tackle socio-political issues (Chon & Park, 2020; Valenzuela, 2013).
Social media platforms offer different affordances and cannot be seen as one entity (Kim & Lee, 2016; Halpern, Valenzuela & Katz, 2017; Valenzuela, Correa & De Zúñiga, 2018). Nevertheless, social media are still treated as a homogeneous medium in most literature on social media and political participation (Velasquez, Wash, Lampe, & Bjornrud, 2014). Therefore, this study takes differences between social media platforms into account. As every social media platform has its own structure and affordances, the routes to online political participation differ as well (Halpern et al., 2017; Valenzuela et al., 2018). This study measures Twitter and Facebook separately in regards to activism and compares them. It fills a gap in the literature, because it looks at the heterogeneity of social media and combines this with a focus on the drivers of social media activism.
The research question of this study is: What explains social media activism on Facebook and Twitter? To answer the research question, this study looks at intrinsic and extrinsic motivations in relation to Facebook and Twitter activism separately. Secondly, it looks at the relationship between internal political efficacy, collective political efficacy and social media political efficacy in relation to Facebook and Twitter activism. Lastly, it compares Twitter and Facebook by using the effect sizes of the relationships. An online survey was distributed to collect data amongst Dutch youth.
Theoretical framework
Social media and political participation
Political participation entails all types of behaviour that aim to influence political institutions and policy (Verba et al., 1995). In the digital age, political participation is no
longer limited to high-efforts and offline acts such as protesting on the streets, joining a political party, or voting. Political participation has enlarged and includes not only these traditional activities, but also similar activities facilitated by online media and new emerging activities (Gibson & Cantijoch, 2013; Lilleker & Koc-Michalska, 2017; Visser & Stolle, 2013). Social media use has been found to positively associate with political participation in the online and offline realm (Boulianne, 2015; Skoric et al., 2016).
This study focuses on the activist dimension of political participation, which implies protesting activities. This are activities “to make one’s voice heard or to make a difference by individual or collective means” including social movements, signing petitions and strikes (Ekman & Amnå, 2012, p. 292). Social media facilitate activism, as they organize and
mobilize individuals (Valenzuela, 2009). For this reason, social media as communication and information tools, have become the core of activism in the digital age (Chon & Park, 2019). Individuals can not only send and receive messages to others, the platforms also facilitate collective action as political expression and deliberation is easy, reduce time and costs and also successfully deal with cognitive constraints of individuals (Bimber, Flanagin, & Stohl, 2005). The interactive and expressive activities facilitated by social media were the
foundation of social movements such as #BlackLivesMatter and the Arab Spring Revolution (Harlow, 2012).
Facets of social media activism such as liking and sharing have been criticized as slacktivism (Kristofferson, White & Peloza, 2014). These activities are seen as lazy, feel-good and low investment politics that do not lead to any real-world change or more meaningful ways of engagement (Gladwell, 2010; Morozov, 2009). Research confirmed that slacktivism not always directly translates to offline actions (Bell, 2014). Contrarily, scholars also found that slacktivism is positively associated with offline actions such as demonstrating or signing petitions (Conroy, Feezell & Guerrero, 2012; De Zúñiga, Jung, & Valenzuela, 2012). Social
media activism solely is also acknowledged as a new form of activism through which big things can happen (Miller, 2017). Margetts et al., (2015) argue that, although all these small online actions might look unimportant, they can create large mobilizations when put together. The critique of slacktivism also ignores the variety of factors that contribute to a political outcome (Dennis, 2018). Seeing political participation as a process, consequently, raises the importance of social media as a platform for spreading knowledge, raising awareness, political discussion and self-expression (idem, 2018).
The conceptualization of social media activism
Social media activism is a form of connective-type collective action (Bennet & Segerberg, 2011, 2012). Bennet and Segerberg (2012) argue that there are two logics: the logic of collective action and the logic of connective action. The logic of collective action is characterised by organizational control and a collective identity amongst people, whereas connective action is based on online media that facilitate networked action (Lundgaard & Razmerita, 2016). Two dynamics that are fundamental to this logic of connective action are personalized frames through personal communication practices and large-scale fluid loose connections networks (idem, 2016). The former indicates that the collective identity is derived through various large-scale personal expressions in contrast to a group identity. The latter refers to interactive communication practices and less hierarchy (Bennet & Segerberg, 2012). Action is connected, as individuals can interact with the personal expressions of others in the network and can alter these messages when distributing them (idem, 2012). Connective action creates a “connective public good”, a shared space of personal expressions, which can
function as an informational resource to enlarge visibility and to connect other individuals by disseminating the message (Shumate & Lipp, 2008, p. 179). In this study, social media activism is defined as the participation of individuals through communicative actions on
social media to collectively tackle socio-political issues (Chon & Park, 2020). There are four different categories of social media activism: commenting, distributing, creating and
affiliating (Nekmat, Gower, Zhou & Metzger, 2019). Hashtag activism is an extension the third category and a new emerged form of social media activism, which is defined as a “discursive protest on social media united through a hashtagged word, phrase or sentence” (Yang, 2016, p. 13). It is only applicable to the realm of social media through its interactive processes.
Social media heterogeneity
Social media platforms offer affordances. Affordances are used to describe the various possibilities for action that a platform offers (Bucher & Helmond, 2017; Faraj & Azad, 2012). In the context of social media, there is a focus on technological affordances and social
affordances (Butcher & Helmond, 2017). Technological affordances imply the affordances offered by the features of new technology. An example is the broadcast function of Facebook, through which someone can share a live event. Social affordances are defined as “social structures that take shape in association with a given technical structure” (Postigo, 2016, p. 5). It refers to the possibilities that technology provides for social relations and structure, of which the forming of relationships is an example (Baym, 2010; Postigo, 2016). This is similar to the abstract and concrete focus in the literature (Butcher & Helmond, 2017). The former entails the dynamics and conditions that are created by technology, whereas the latter entails the materiality of a technology as buttons and screens.
In this study Facebook and Twitter are compared. As Facebook and Twitter differ mostly in their social affordances, this study focuses on the social affordances of social media (Valenzuela et al., 2018). They therefore cannot be seen as homogeneous (Halpern et al., 2017; Valenzuela et al., 2018). Moreover, social media activism is defined as connective-type
collective action based on social networks of interaction. Facebook and Twitter differ in network structure and relationship forming (Kim & Lee, 2016; Valenzuela et al., 2018). On Facebook you only become friends when both agreeing to it. This reciprocal ‘friendship’ is not needed on Twitter (Kim & Lee, 2016). An individual can follow another person without this person following back (Valenzuela, et al., 2018). Chen argues (2011) that Facebook offers two types of relationships (friends/no friends), albeit Twitter offers four types (you → other, you ← other, you ↔ other, no relationship). These different ways of connecting can influence from whom information is received and to whom information is disseminated. The more types of relationships a platform offers, the more diverse its network and subsequently the information and interactions (Kim & Lee, 2016).
As a result of this relationship forming process, scholars argue that Twitter is based on weak-tie networks whereas Facebook specializes on strong-tie networks (Valenzuela et al., 2018). Strong ties are intimate connections such as family and friends, weak ties are distant connections such as acquaintances (Granovetter, 1983; Kenny, 1994). On Twitter it is easier to follow weak ties, such as politicians, but people on Facebook tend to have a connection with individuals with whom they already have a real-life relation (Ellison, Steinfield & Lampe, 2007). Both ties can have different effects on collective action. The power of strong ties is its social influence on people’s behaviour, which is greater than with weak tie networks (Straits, 1991). Thus, this effect also counts for activism, as this dimension of political
participation needs social reinforcement and pressure (Valenzuela, Arriagada & Scherman, 2014). Another outcome, however, is that strong ties entail a more homogenous network with little diversity in political views and information (De Zúñiga & Valenzuela, 2011).
Nevertheless, Saffer (2016) argues that weak ties promote collective action. Loose connection networks offer an efficient platform for the dissemination and reception of information which
can subsequently lead to mobilization (Valenzuela et al., 2018). Thus, results are incoherent regarding ties and collective action.
Motivations and online political participation
What motivates people to participate in collective action has been examined by scholars frequently. In the last decade, this focal point has however shifted to the exploration of new relationships between social media and political participation (Chon & Park 2019). Therefore, there is a lack of research on the motivations that drive online political
participation (Lilleker & Koc-Michalska, 2017). Disclosing the motivations for political participation is important, as political participation is the key for democracy (Yamamoto, Kushin, & Dalisay, 2015). Social media facilitate and ease the extensive distribution of information on socio-political issues, which makes it essential to understand what brings people to these actions (Chon & Park, 2020). Knowing why people engage with politics in the online world also creates the ability to see if and in what ways activism has changed in the digital age.
Intrinsic and extrinsic motivations
Motivations perform the interaction between personal attitudes and external persuasion (Ryan & Deci, 2000b). Intrinsic motivations focus on personal attitudes leading to an evaluation of certain actions. It is an evaluation whether a certain action is personally
satisfying (Ryan & Deci, 2000a). When intrinsic motivations are involved, activities are done for the positive experiences that arise while doing so (idem, 2000a). In relation to collective action, intrinsic motivations can explain why individuals contribute to collective action in contrast to freeriding behaviour (Hirschman, 1977). Extrinsic motivations hinge upon the confirmation of social norms people are searching for when making behavioral decisions
(Lilleker & Koc-Michalska, 2017). They have a focus on the instrumentalist base of
motivation, as people are always in search of approval and recognition of others (Deci, 1971). Extrinsic motivations, in the realm of social media activism can for example explain why someone shares a post via social media, because this is highly normative in this person’s social network. Social networks can motivate individual behaviour, as collective opinions and perspectives can help someone explore and recognize socio-political issues (Glynn & Park, 1997). Lilleker and Koc-Michalska (2017) demonstrate that both intrinsic and extrinsic motivations are associated with online political participation, with greater explanatory power for extrinsic motivations. The first two hypotheses are thus stated as follows:
H1: There is a positive relationship between intrinsic motivations and Facebook activism (a) and Twitter activism (b), in a way that more intrinsic motivations imply more activism. H2: There is a positive relationship between extrinsic motivations and Facebook activism (a) and Twitter activism (b), in a way that more extrinsic motivations imply more activism.
Efficacy perceptions and online political participation
Various studies have looked at the relationship between political efficacy and collective action. This study focuses on three types of efficacy: self-efficacy, collective efficacy and social media efficacy.
Self-efficacy and collective efficacy
Self-efficacy implies an individual’s belief and perception in the ability to participate in politics (Caprara, Vecchione, Capanna & Mebane, 2009). It is found to be necessary for political participation, because a feeling of competence is the incentive to participate (Aldrich, 1982). Many studies found that self-efficacy is a cognitive key predictor of political
participation and is fundamental to action (Bandura, 1997; Solhaug, 2006). Results of the relationship between self-efficacy and online political participation are mixed, as not all scholars found evidence of a relationship (Velasquez & LaRose, 2015b). This might be due to different conceptualizations of political participation in various studies.
Collective efficacy is an indicator of people’s beliefs and perceptions regarding the capacity of a group to organize and mobilize collective action (Kavanaugh, Reese, Carroll, & Rosson, 2005; Velasquez & LaRose, 2015b). Many political issues can only be solved by a collective effort and a shared belief; people do not live in a vacuum (Caprara et al., 2009; Halpern et al., 2017). As social media can mobilize people from all over the world, it is probable that collective efficacy increases social media activism. According to Velasquez & LaRose (2015b), there is a positive relationship between collective efficacy and connective-type collective action. You and Hon (2019) also argue that collective efficacy is positively correlated with online activism, which leads to the following hypotheses:
H3: There is a positive relationship between self-efficacy and Facebook activism (a) and Twitter activism (b), in a way that more self-efficacy implies more activism.
H4: There is a positive relationship between collective efficacy and Facebook activism (a) and Twitter activism (b), in a way that more collective efficacy implies more activism.
Social media political efficacy
Social media political efficacy indicates the perceived capability of individuals to use social media in an effective way to achieve political objectives (Velasquez & LaRose, 2015a). Therefore, the concept is an extension of self-efficacy, adapted to the social media context. In the digital age, it has become easier to spread information. It might be probable that people who have strong beliefs in their effective use of social media will use it more for activism
(Chon & Park, 2020). Velasquez & LaRose (2015a) found a strong relationship between social media efficacy and social media activism; stronger than the relationship between self-efficacy and social media activism. Chon & Park (2020) also recognize social media self-efficacy as a strong predictor for social media activism, leading to the following hypothesis:
H5: There is a positive relationship between social media political efficacy and Facebook activism (a) and Twitter activism (b), in a way that more social media political efficacy implies more activism.
Facebook versus Twitter
The network structures of Twitter and Facebook differ, which makes the platforms well suited for a comparison in this study. Twitter is a microblogging platform, whereas Facebook is a social networking site. Both platforms are also widely used for political purposes in contrast to platforms as Instagram and Snapchat (Halpern et al., 2017). For this reason, the relation between extrinsic and intrinsic motivations and activism might differ per platform. As Facebook is more homogenous and based on close ties, it is proposed that the relationship between extrinsic motivations and activism is stronger on this platform, whereas for Twitter the relationship between intrinsic motivations and activism is stronger. Halpern et al. (2017) demonstrate that people who share political content on Twitter feel more
efficacious in the internal realm, whether people feel more efficacious in the collective realm on Facebook. Most research provides evidence that Twitter is more often used for political purposes than Facebook (Jungherr, 2016; McGregor, Mourão & Molyneux, 2017). The platform might accordingly give the feeling that it is an effective tool for activism. Therefore, there are different expectations regarding the strength of the relationships:
H6: In H1 (intrinsic motivations), H3 (self-efficacy) and H5 (social media efficacy) the relationship is stronger for Twitter (b), whereas in H2 (extrinsic motivations) and H4 (collective efficacy) the relationship is stronger Facebook (a).
Method
To test the hypotheses, four multiple logistic regression analyses are conducted. The first two analyses included the two motivational explanatory variables for Facebook and Twitter activism. The last two analyses included the efficacy explanatory variables for Facebook and Twitter activism.
Sample
In order to participate in this study, respondents had to be Dutch and 18 to 27 years old. Young people are more active on social media than other parts of the population and have been raised with social media as a tool. Millennials are also powerful agents of change (Kanter & Fine, 2010). A convenience sample and a snowball sampling are used to find respondents at relatively low costs and high speed. An online survey is distributed via the Facebook1 and Twitter2 page of the author and via WhatsApp between the 4th of May and the
29th of May. On the 4th of May, the online survey was sent via the WhatsApp of the author to
a total of 15 WhatsApp groups with at least four people in it. 20 close friends and family members also received a private message via WhatsApp of the author in which they were asked to send the survey to at least 5 other people. On the same day, the survey was shared on the personal Facebook page and Twitter Page of the author. In the third week of the data collection, again 10 close friends and family members received a private message of the
1 The author has 786 friends on Facebook. 2 The author has 102 followers on Twitter.
author via WhatsApp with a request to forward the survey to at least 5 other people. This was repeated, since messaging friends and family personally via WhatsApp proved to be very effective. The Facebook post was shared by two people in the author’s network. The Twitter post was shared for a second time on the 19th of May. Furthermore, the survey was shared
alongside a message from the author, which contained the mandatory age range and the fact that a 20-euro gift card of choice would be raffled amongst the respondents.
In total, 246 people participated in the online survey (N = 246). A total of 190 respondents remained as valid respondents and is the final sample of this study (N=190).3
Design
To gather data and test the hypotheses, an online survey is created via Qualtrics. An online survey is a relatively fast and cheap way to collect data and creates the ability to spread the survey via WhatsApp and social media. This online design fits the research question of this study, since respondents are politically participating via digital media. The survey is cross sectional and included 17 self-report questions, some with multiple statements. The survey can be found in appendix A. Beforehand, respondents needed to agree with the consent and the terms and conditions of the study. At the last page, respondent could fill in their email address for the raffle of the gift card.
Descriptive statistics
Of all respondents, 27.4% is male and 72.6 % is female. Hence, the sample included more women than men. The average age is 23 years (SD = 1.85). 78.9%, a vast majority, indicated that they finished a bachelor’s degree or higher. This implies that most of the
participants enjoyed higher education. 52.1 % of the respondents is affiliated with the left side
3 41 respondents did not complete the survey or did not approve the terms and conditions of the survey. Another
of the political spectrum and 20.5% with the right spectrum. This means that a substantial part of the respondents is left oriented. More than half, 58.4%, has not done any offline political related activities in the past six months. Of all respondents, 189 people have Facebook and actively use it on a daily basis (n = 189). Only 49 people have Twitter and actively use it on a daily basis (n = 49). Interestingly, there were no respondents that have Facebook and do not use it, whereas 21 respondents have Twitter and do not use it. Users tend to spend the same amount of time on Twitter as on Facebook, which generally is less than 30 minutes a day. 35 Twitter users engaged in Twitter activism, whereas 110 Facebook users engaged in Facebook activism. Facebook users are compared with Twitter users, respondents that use both are included in the analysis for Facebook and for Twitter. Table 1 in appendix B provides all variables with means, standard deviations and scale ranges.
Measures
To explore if scales could be created for Facebook and Twitter activism, intrinsic and extrinsic motivations, self-efficacy, collective efficacy, social media efficacy and offline political participation, a factor analysis and a reliability analysis were performed. An overview of all scores of the factor and reliability analyses can be found in appendix E. All questions and statements with their corresponding measures can be found in appendix A.
Dependent variables
As multiple statements refer to socio-political issues, a definition of a socio-political issue is provided in the survey to avoid misconceptions amongst the respondents. It stated: “A goal is socio-political if it consists of political factors and social factors. A socio-political topic is often of great societal importance in which politics plays a key role. Some examples are the environment, inequality, human rights or discrimination”.
Facebook activism and Twitter activism. There are four categories of social media activism: commenting, distributing, creating and affiliating (Nekmat et al., 2019). All four categories resemble one statement to measure activism. One last statement is included, based on hashtag activism (Yang, 2016). For every statement, respondents could assess if they engaged in the proposed activity or not (yes = 1, no = 0). In total, five items measured each variable and were combined into two scales. The statements were the same for both variables, only the name of the platform and the activity were altered.4 The constructed scale for
Facebook activism is reasonably reliable, Cronbach’s Alpha = .655. The Twitter activism
scale is reliable, Cronbach’s Alpha = .77. Both scales were changed into the binary variables Facebook and Twitter activism, in order to use it in a multiple logistic regression. If a
respondent engaged in at least one activity it was coded 1, if not it was coded 0 (yes = 1, no = 0). Both scales were also changed into the binary variable degree of Facebook and Twitter activism, that excluded the respondents that did not engage in activism.6 If a respondent
engaged in 1 or 2 activities, it was coded 0, if a respondent engaged in 3, 4 or 5 activities it was coded 1 (0 = little activism, 1 = much activism).
Independent variables
Intrinsic motivations. This concept is measured with the Intrinsic Motivation Inventory (IMI), which consists of multiple statements that focus on factors that supply self-satisfaction (McAuley et al., 1989, Ryan, 1982). The index consists of seven subscales, but this measurement focuses on three subscales: interest/enjoyment, value/usefulness and
4 If someone for example affiliates with a post on Facebook this is referred to as ‘liking’, on Twitter this is called
‘favouriting’.
5 The scale could be improved by deleting one item. There is, however, chosen not to, because Twitter and
Facebook are compared and this can best be done by having the same number of items that measure similar activities.
perceived choice, as they were found most suitable for social media activism. Five items measured this variable amongst respondents that engaged in at least one activism activity7.
For every statement, respondent could assess their level of agreement with a five-point Likert scale (1 = strongly agree to 5 = strongly disagree). Four items remained to form a scale, which is good and reliable, Cronbach’s Alpha = .82.
Extrinsic motivations. Extrinsic motivations are measured based on the three
different facets distinguished by Deci and Ryan (2000b). There is a focus on two subscales, as they fit best with social media activism: external regulation, behaviour regarding receiving awards or punishment, and introjected regulation, behaviour due to pressure rather than own choice (Deci & Ryan, 1985). Five statements measured extrinsic motivation amongst the respondents that engaged in at least one activism activity. For every statement, respondents could assess the level of agreement by use of a five-point Likert scale (1 = strongly agree to 5 = strongly disagree). A reliable four-item scale was constructed, Cronbach’s Alpha = .72.
Self-efficacy. To measure this concept, the four-item scale of Morrel (2005) is used. For every statement, respondents could assess the level of agreement with a five-point Likert scale (1 = strongly agree to 5 = strongly disagree). A reliable scale is constructed, Cronbach’s alpha = .80.
Collective efficacy. The scale by Halpern et al. (2017) is used with five statements, due to the similarity between studies. For every statement, respondents could assess their level of agreement with a five-point Likert scale (1 = strongly agree to 5 = strongly disagree). All items were combined into one reliable scale, Cronbach’s Alpha = .89.
Social media political efficacy. This concept is measured based on the four-item scale of Velasquez & LaRose (2015a) and Bandura (2006). For every statement, respondent could
7 The statements that measured intrinsic and extrinsic motivations included the specific involvement in
socio-political issues. Presuming that when the activism activity is mentioned, respondents better understand the statement.
assess the level of agreement with a five-point Likert scale (1 = very certain to 5 = very uncertain). The scale is reliable, Cronbach’s Alpha = .87.
Control variables
Gender, age, education and political affiliation were included in the analyses as socio-demographic variables. As this study focuses on online political participation, its offline counterpart offline political participation was measured as well (De Zúñiga, Molyneux and Zheng, 2014; Lilleker & Koc-Michalska, 2017). Furthermore, network heterogeneity was included, because the concept measures network structure (Scheufele, Hardy, Brossard, Waismel-Manor & Nisbet, 2006). Lastly, Facebook and Twitter use were measured
separately. This was only done if the respondent already indicated to have the corresponding social media platform. In the analyses for Facebook, Facebook use was included in the Facebook analysis and Twitter use was included in the Twitter analysis. Full details of all control variables can be found in appendix C.
Results
To explore the direction and the strength of the relationship between the dependent and independent variables, Kendall’s-Tau is calculated. To subsequently get a more detailed look of the data and determine whether the independent variables predict Facebook and Twitter activism, a multiple logistic regression is performed for Facebook and Twitter activism. Regression analysis is a stronger and more demanding test and therefore used as a more advanced analysis. Two multiple logistic regression analyses were performed for intrinsic and extrinsic motivations with the dependent variable degree of activism, as
motivations were only measured for respondents that engaged in activism. Subsequently, two multiple logistic regression analyses were performed for the efficacy types and the dependent
binary variable activism.
For Facebook activism, the distribution of residuals was not satisfactory regarding homoscedasticity, not normally distributed and right skewed. This is visible in the histogram of Facebook activism in appendix F and implies that the data has a greater number of
occurrences in the low values and less in the high values.8 For Twitter activism, the residuals
were normally distributed and met the assumption of homoscedasticity. As one of the dependent variables did not meet the assumptions for a linear regression analysis, a multiple logistic regression analysis is performed. The variables were also tested for multicollinearity, see appendix F.9
1. Correlation matrix
To determine the correlations between the dependent and independent variables, a correlation matrix is created including the means, standard deviations and Kendall’s Tau coefficients in appendix D. This correlation coefficient is used, as it is insensitive to errors and has a smaller asymptotic variance than Spearman’s Rho, which makes it more robust and efficient. Furthermore, the p-values of this correlation coefficient are more accurate because of the small sample size in this study. Kendall’s Tau-b is used, since the scales of the main predictors are all ordinal and have the same range of values.
The correlation between intrinsic motivations and Twitter activism degree is not significant, negatively weak, Tau-b = -.039, p > .05. For Facebook activism degree this correlation is positive, but weak, Tau-b = .171, p > .05. The correlation between extrinsic motivations and Twitter activism degree is also not significant, but negative and weak, Tau-b = .208, p >. 05. The correlation between extrinsic motivations and Facebook activism degree
8 74.1% of the respondents indicated that they not engage in any activist activity or that they engaged in only 1
activist activity on Facebook.
9 The variance inflation factor (VIF) is calculated for all independent and control variables. All of the VIF scores
of the independent and control variables were less than 5, which implies that there is no indication of a problem with multicollinearity.
is weak, positive and not significant either, Tau-b = .160, p > .05. These results indicate that there is no significant relationship between the two types of motivations and social media activism on both platforms.
However, there is a significant correlation revealed between self-efficacy and Twitter activism, Tau-b = .357, p < .01, and self-efficacy and Facebook activism, Tau-b = .184, p < .01. The former correlation is moderately weak and positive, the latter correlation is weak and positive. For collective efficacy and Facebook activism, a positive weak correlation is found, Tau-b = .126, p < .05. The correlation between collective efficacy and Twitter activism is also significant, Tau-b = .262, p < .05, positive, but weak. Moreover, the results reveal a
significant positive weak correlation between social media efficacy and Facebook activism, Tau-b = .249, p < .001. For the correlation between social media efficacy and Twitter activism a significant, positive moderately weak, result is found, Tau-b = .342, p < .01.
2. Multiple logistic regression
Based on the information retrieved from the correlation matrix, a multiple logistic
regression analysis is run to determine whether the independent and control variables predict Facebook and Twitter activism. Because intrinsic and extrinsic motivations are not
significantly correlated with activism, the multiple logistic regression for motivations is not elaborated in this section but in appendix G. An explanation for these results might be the small sample of Twitter users engaging in activism and the relative high number of explanatory variables. H1 and H2 proposed a positive relationship between intrinsic and extrinsic motivations and Facebook activism (a) and Twitter activism (b). H1 and H2 are therefore rejected and will not be compared for H6 either.
In order to test H3, H4 and H5 for Facebook activism (a), a multiple logistic
regression analysis was run. Table 2 provides the results of the multiple logistic regression. H3, H4 and H5 (a) proposed a positive relationship between self-efficacy, collective efficacy, social media efficacy and Facebook activism.
Table 2
Logistic regression of Facebook activism
***p < .001 **p < .01 *p < .05
Note. The dependent variable in this analysis is Facebook activism so that 0 = no activism
and 1 = activism, OR = odds ratio, R2 = Nagelkerke.
The model significantly predicts Facebook activism (omnibus chi-square = 47.155, df = 10, p < .001). It predicts 54.4% of the non-activist activities and 80.9% of the activist
b* p-value Odds Ratio
Model 1
Constant -3.607 .245 .027 Self-efficacy .493* .034 1.637 Collective efficacy .193 .532 1.213 Social media efficacy .625** .001 1.868
Woman .853* .041 2.346 Age -.034 .744 .966 Education -.010 .961 .990 Political affiliation -.056 .557 .945 Offline political participation .633** .002 1.883 Heterogeneity -.030 .804 .971 Facebook use -.015 .931 .985 Model omnibus chi-square = 47.155, p < .001
R2 = .297 n = 189
activities. Overall, 69.8% of the predictions are correct. 29.7% of the variance in the dependent variable can be explained by this model (Nagelkerke R2). Two independent and two control variables are found to be significant predictors of Facebook activism. An increase in self-efficacy increases the likelihood of Facebook activism (OR = 1.637, p < .05). This implies that the probability of Facebook activism inclines when self-efficacy increases. If your feeling of self-efficacy increases, it puts you at 1.64 greater odds to engage in Facebook activism. H3 (a) is accepted. For social media efficacy an increase also extends the likelihood of Facebook activism, as the odds ratio is higher than 1 (OR = 1.868, p < .001). The
probability of Facebook activism rises when social media efficacy increases. Every increase in social media efficacy puts you at 1.87 greater odds to engage in Facebook activism. H5 (a) is therefore accepted. Collective efficacy is not a significant predictor of Facebook activism. t however, is weakly correlated when not controlling for other variables. H4 (a) is rejected. Furthermore, woman and offline political participation proved to be significant predictors of Facebook activism. Being a woman puts you at 2.35 greater odds to engage in Facebook activism (OR = 2.346, p < .05). This correlation was, however, not significant in the correlation matrix. The probability of Facebook activism increases if offline political participation increases (OR = 1.883, p < .01). In conclusion, H3 (a) and H5 (a) are accepted and H4 (a) is rejected.
In order to test H3, H4 and H5 for Twitter activism (b), a similar multiple logistic regression analysis as for Facebook is run. Table 3 provides the results of the logistic regression. H3, H4 and H5 (b) proposed a positive relationship between self-efficacy, collective efficacy, social media efficacy and Twitter activism.
Table 3
Logistic regression of Twitter activism
b* p-value OR
Model 1
Constant -2.974 .851 .051 Self-efficacy 3.126* .013 22.775 Collective efficacy -.212 .820 .809 Social media efficacy 2.565* .046 13.002 Woman 3.592 .084 36.318 Age -.307 .639 .736 Education -2.003 .058 .135 Political affiliation -.409 .410 .664 Offline political participation 1.270 .099 3.562 Heterogeneity .275 .474 1.317 Twitter use 1.677 .350 5.349 ***p < .001 **p < .01 *p < .05
Note. The dependent variable in this analysis is Twitter activism so that 0 = no activism
and 1 = activism, OR = odds ratio, R2 = Nagelkerke
The model significantly predicts Twitter activism (omnibus chi-square = 31.669, df = 10, p <.001). It successfully predicts 78.6% of the non-activist activities and 91.4 % of the activism activities. Overall, 87.8% of predictions are correct. 68.2% of the variance in the dependent variable can be explained by the independent and control variables (Nagelkerke R2). Significant results are revealed for efficacy and social media efficacy. More self-efficacy highly increases the likelihood of Twitter activism (OR = 22.775, p < .05). This implies that the probability of Twitter activism rises when self-efficacy increases with one unit. Every increase in self-efficacy puts you at a 22.78 greater odd to probability to engage in
Model omnibus chi-square = 31.669, p < .001 R2 = .682
Twitter activism. H3 (b) is therefore accepted. The odds ratio of social media efficacy is far above 1, which means that more social media efficacy inclines the likelihood of Twitter activism (OR = 13.002, p < .05). This implies that the probability Twitter activism increases when social media efficacy increases with one unit. Every increase in social media efficacy puts you at 13.00 greater odds to engage in Twitter activism. H5 (b) is accepted. H4 (b) is rejected, because collective efficacy is not a significant predictor of Twitter activism.
Collective efficacy, however, is weakly correlated when not controlling for other variables. It therefore still is an interesting variable in regards to activism. H3 (b) and H5 (b) are accepted, H4 (b) is rejected.
Lastly, Facebook and Twitter activism are compared. A paired sample t-test revealed that there is no significant difference between the scores of Facebook activism (M = 1.54, SD = 1.49) and Twitter activism (M = 1.85, SD = 1.69) in this sample, t(47) = -1.528, p > .05. The unstandardized effect sizes of the two significant predictors activism of Facebook (a) and Twitter (b) activism are also compared to answer H6. As both predictor variables are
measured on the same scale, the unstandardized effect sizes are used. Also, this effect size is better interpretable. H6 proposed that the relationship between self-efficacy and activism is strongest for Twitter and that the relationship between social media efficacy and activism is strongest for Facebook. Table 4 provides the odds ratios of self-efficacy and social media efficacy.
Table 4
Odds ratios for self-efficacy and social media efficacy Facebook activism OR CI 95% Twitter activism OR CI 95% Self-efficacy 1.637* 1.037, 2.582 22.775* 1.918, 270.380 Social media efficacy 1.868** 1.275. 2.737 13.002* 1.042, 162.243 **p < .01 *p < .05
The CIs do not include 1, which means that the odds ratios are statistically significant and there might be a difference. For every increase in self-efficacy, the probability of
Facebook activism increases by a factor of 1.64, whilst the probability of Twitter activism increases by a factor of 22.78. However, important to note is that the CI for Twitter is very wide. This indicates a high uncertainty, because it is the range of values the true value in the population is expected to fall within. Thus, it can be acknowledged that the effect sizes widely differ, but with high uncertainty. H6 regarding self-efficacy is therefore only partly
confirmed. For every increase in social media efficacy, the probability of Facebook rises with a factor of 1.87, whereas the probability of Twitter activism rises with a factor of 13.00. The CIs fully overlap suggesting an insignificant difference. H6 regarding social media efficacy is also partly accepted. The strength of the relationship between social media efficacy and activism is strongest for Twitter. Again, the CI for Twitter is very wide and even includes the whole Facebook CI.
Conclusion and discussion
This study focused on the motivations and explanations of young people that engage in social media activism. Due to the rise of the internet, new forms of political participation have emerged including social media activism. It is important to know what motivates people to engage in social media activism, as social media facilitate information dissemination on socio-political issues. Facebook and Twitter, two social media platforms, were looked at separately regarding activism and were compared to explore if the strengths of the
relationships differ between platforms. A comparison like this has not been made in previous research, especially not with a focus on the explanations and motivations of social media activism. Social media offer different affordances that differentiate a microblogging platform
like Twitter from a social networking site as Facebook. This study therefore is a valuable contribution to the literature.
No significant difference has been found between respondents’ engagement in Facebook and Twitter activism. Hence, a conclusion regarding a preferation amongst platforms to use for political purposes cannot be drawn. This study focused on two types of motivations and three types of efficacy in relation to activism. No evidence is found that intrinsic and extrinsic motivations are significant predictors of Facebook or Twitter activism. These results are striking, since a recent study found a relationship between both types of motivations and online political participation (Lilleker & Koc-Michalska, 2017). This study conceptualized online political participation as activism regarding socio-political issues, which might have led to this difference in outcome. That neither intrinsic nor extrinsic
motivations are predictors of Facebook and Twitter activism leaves a question mark regarding young people’s motivations to engage in social media activism. Qualitative research might be valuable to analyse what motivates young people to perform online activist activities, because it might go beyond these two generic types of motivation. Bimber et al. (2005) for example mention that socializing and affiliating with others can be a strong motivation for social media activism, as it is built upon the interaction with others. Bennet and Segerberg (2012) argue that it can also be an act of personal expression and self-validation. Furthermore, social media activism is a new phenomenon and can be, due to its communicative action in comparison to its physical offline counterpart, performed unintentionally. Nevertheless, this study offers alternative explanations why people engage in social media activism.
Aligned with the literature, this study found that self-efficacy and social media efficacy are positive predictors of Facebook and Twitter activism (Chon & Park, 2020; Velasquez & LaRose, 2015a). The results suggest that a greater feeling of self-efficacy and social media efficacy increase the likelihood of engaging social media activism. Self-efficacy
and social media efficacy are closely related, but when it comes to personal political abilities this distinction is important. There might be individuals that do not feel that they have the ability to participate in offline activism, while they might feel this way when using social media. However, the results of this study reveal that both are important regarding social media activism. As social media efficacy is a more elaborative predictor than solely self-efficacy, it valuably extends efficacy measures for the digital age in which social media cannot be left out.
Collective efficacy is no revealed predictor of Facebook and Twitter activism. This is in contrast with previous literature and can be explained by the fact that this study focused on youth in general instead of politically active youth and on real behaviour instead of intentions (Velasquez & LaRose, 2015b; You & Hon, 2019). This finding might also demonstrate that there is a different feeling of ‘the collective’ when engaging in social media activism in comparison to offline activism. A shared collective identity has been replaced with individual frames of expression in the logic of connective action (Bennet & Segerberg, 2012). However, it is weakly related with activism on both platforms which should not be downplayed. It still is an interesting variable in regards to activism, but not necessarily as an explanation for it. It might for example be something that arises from engagement in social media activism, due to its interactive structure.
Lastly, the effect sizes of Facebook and Twitter were compared. Self-efficacy proves to be the strongest predictor for Twitter activism. This finding equals the study of Halpern et al. (2017) and might be due to the individual microblogging nature of Twitter, whereas Facebook is mostly based on existing relationships. Social media efficacy is also the strongest predictor for Twitter activism. The high level of uncertainty regarding these results, however, should be taken into account. This study found a difference between both platforms which should definitely be further studied.
Limitations
This study has its limitations that need to be addressed. First of all, the sample size of 190 respondents is rather small. Besides that, there were only 49 respondents that have Twitter and actively use it on a daily basis. For Facebook, only one person did not actively use it on a daily basis. A substantial part of the respondents is found through Facebook, which has contributed to this large group of Facebook users. With such little sample size, especially for Twitter, it is difficult to find significant results. Consequently, significant results were found in this study paired with a relatively high amount of uncertainty. Further research should focus on a bigger sample size and more variation between respondents, especially regarding Facebook activism. It would also be interesting to create different groups and compare people that only use Twitter, people that only use Facebook and people that use both. This is only possible in a study with a larger sample size.
Secondly, the external validity of this study is relatively low and only holds for a specific subgroup in the population: youth. The survey was spread via social media, using a non-probability sample and is thus related to the social circle of the author. It is therefore plausible that the respondents are not a representative sample of the subgroup. This is also exposed by the descriptive of the statistics; the majority of the respondents has finished higher education. Furthermore, future research should focus on new emerging social media platforms such as Instagram. Twitter use in The Netherlands has been declining in the last couple of years. Instagram, on the contrary, has been growing and offers other affordances than the platforms in this study. Such additions can deepen knowledge on activism in the digital age. Moreover, future studies should include other explanatory variables that can help explain why people engage in online activism. Other scholars for example suggest that situational
variables related to the socio-political topics can be helpful predictors of social media activism (Kim & Grunig, 2011).
In conclusion, youth actively engage in social media activism. Efficacy types can help explain why people engage in social media activism. In this study, self-efficacy and social media efficacy predict activism on Facebook and Twitter. The more self-efficacy and social media efficacy, the higher the likelihood to engage in activism on both platforms. Self-efficacy and social media Self-efficacy are a stronger predictor for Twitter than for Facebook activism.
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Appendix A
The Survey (translated from Dutch) Part 1. Consent
Hi everyone!
I would like to invite you to participate in this research study that is carried out with the support and approval of the Department of Communication science of the University of Amsterdam. This study will give an insight on political participation through social media platforms. Participating in the survey takes about 7 minutes. A gift card of choice, worth of 20 EUR, will be awarded to one of the participants when all data is obtained. You can participate if you are 18 to 27 years old.
As this research is being conducted with the support and approval of the Amsterdam School of Communication Research (ASCoR), University of Amsterdam, I can guarantee that:
• The anonymity of participants in the research will be safeguarded, and that personal information will never be passed on to third parties without their permission, unless you first explicitly give permission for it.
• Participation in this study is voluntary. Participants can thus always refuse to participate and can pull back at any time. They furthermore have the option to withdraw their permission for the use of their data up to 7 days after completing the study. There are no further consequences related to these actions as well as no reason needs to be given to choose for these actions.
• As a participant, you are entitled to receive a summary of the results of the study when these are available. If you would like so, please contact Iris van Eijck (contact details below).
Should you have any complaints or comments about this research, you can contact the designated member of the Ethics Committee representing the Department of Communication Science, at the following address: ASCoR Secretariat, Ethics Committee, University of Amsterdam, Postbus 15793, 1001 NG Amsterdam; 020‐525 3680; ascor‐secr‐fmg@uva.nl. Any complaints or comments will be treated in the strictest confidence.
Feel free to contact me at if you have any questions regarding this study via: iris.vaneijck@student.uva.nl.
Thank you very much in advance!
Kind regards, Iris van Eijck
---
I hereby declare that I have been informed in a clear manner about the nature and method of the research.
I agree, fully and voluntarily, to participate in this research study. With this, I retain the right to withdraw my consent, without having to give a reason for doing so. I am aware that I may halt my participation in the survey at any time.
If the research results are used in scientific publications or are made public in another way, this will be done such a way that my anonymity is completely safeguarded.
If I have any complaints or comments regarding this study, I know that I can contact the designated member of the Ethics Committee representing the Department of Communication Science, at the following address: ASCoR Secretariat, Ethics Committee, University of Amsterdam, Postbus 15793, 1001 NG Amsterdam; 020‐525 3680; ascor‐secr‐fmg@uva.nl
I know that I can contact iris.vaneijck@student.uva.nl if I have any questions regarding this study.
I have read the above text and:
I agree to participate in the research study I do not agree to participate in the research study
