Masaryk university
Does Weibo Use Affect Collective Action Involvement?
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- Navigate this page:
- Descriptive Statistics Results
- Demographics of Collective Action Involvement
- Logistic Model Test Result
- Weibo Use Boycott
- Weibo Use Petition Signing
- Weibo Use Weibo-driven Events
- Weibo Use Online Protest
Does Weibo Use Affect Collective Action Involvement?Hypothesis 5 predicts that Weibo use for political purposes will lead to more involvement in collective action, including boycotts, petitions, online protests, offline protests, and Weibo-drive events. This section explores whether hypothesis 5 is supported or not.
Figure 3 Collective action involvement Figure 3-5 shows the collective action involvements of Weibo users. Overall, there were 100 respondents who had participated in at least one form of collective action within the past year. There were more people who had participated in either an online protest or Weibo-driven event than participated in a boycott or signed a petition. No respondents in this survey had ever participated in an offline protest. About 20.1 percent of all the respondents report that they had participated in Weibo-driven events within the past year, which is the highest percentage among all. 17.3 percent had joined online protests. Only 11.7 percent (N=21) of respondents had joined a boycott in the past year, and only 6.7 percent (N=12) of them had signed a petition (among these, two were from overseas). Three questions emerged from the figure above: 1. Why had more people participated in Weibo-driven events and online protests? 2. Why had fewer people signed a petition or participated in boycotts? 3. Why had nobody ever participated in an offline protest? First, online protest is one of the easiest and least risky forms of activism Weibo users can join. Just by clicking once, a user can send a protest message to other people. This is also the most common form of activism occurring daily on Weibo. The Weibo-driven events are generally charity oriented instead of politically oriented. Thus, they are often tolerated by the Weibo website as well as the state. The most famous Weibo-driven events include “donate books to rural areas,” “free lunch for primary school kids” and so on. In fact, Sina Weibo has developed a special section of the Weibo-driven charity sub-site to encourage more people to participate. That only a very limited number of people signed a petition within the past year is possibly because: 1. There is no online petition website in the Chinese mainland. All the foreign online petition websites are blocked. 2. Petitions initiated by foreign websites often concern sensitive issues that can be quickly detected by the censor detector and deleted. That no one had ever participated in any offline protest might be due to two reasons: 1. Sampling issue: This research has a large sample from young people who are urban residents with higher education levels. However, in today’s China, the protestors are not highly educated students, but people from disadvantaged groups. They are often marginalized in society and are more vulnerable to serious rights infringements. However, according to the CNNIC (2010) report, most Weibo users are young urban residents, especially students. Thus, even though there are offline protestors who also use Weibo to broadcast their grievances, they only make up a small portion of the total Weibo user population. This research failed to sample this group of people. 2. Government control. The government tends to be cautious about offline protests. After the crackdown of student protests in Tian’an men Square, offline student protests were particularly rare after the 1980s. Massive student protests were staged only a few times; most of them related to nationalism. For example, a massive student protest occurred after NATO bombed the Chinese embassy in Belgrade. Since a large part of this sample was made up of students, government regulation might explain why nobody had joined in any offline protest. In summation, the findings above suggest that Weibo has a very limited effect on mobilizing offline protests in contemporary China. Protesting online or participating in Weibo-driven events are much less risky activities than protesting offline. Most Weibo activists are not willing to risk themselves for contentious actions. We witnessed that villagers in Wukan paid a heavy price for their protest against corruption. Their success might only reflect one single case out of dozens of unsuccessful offline protests. Even though Weibo can disseminate information faster than any other medium, due to strict censorship, as well as regulation of offline protests, it is unlikely that it will have the same mobilizing effect in China we witnessed in the Arab world, at least for now. However, online protests on Weibo can still provide alternative information to Weibo users that would not be seen in other media. This research covered only Weibo users—predominantly highly educated students and white collar workers—who are less likely to participate in offline protests.
Table 3-10 presents the demographics of respondents who had participated in collective action last year. More male than female respondents had participated in boycotts, petitions and online protests within the past year. However, more female than male respondents had participated in Weibo-driven events. A Chi-square was run to test if gender was related to collective action involvement. The results showed no significant relationship between the two variables. Most of the respondents who joined in collective action were between 18 and 25 years old, which is also the largest age cohort in this survey. Meanwhile, the fact that more respondents without income had participated in collective action indicates that more students participated in collective action. Most of them have bachelor’s degrees, also the largest cohort of education level in this survey. However, since individual factors such as gender, age, education, and monthly income had no influence on Weibo activism use, they were excluded in the binary logistic model analyses for predictability analysis. Table 3 Demographics of collective action involvement over the past year
Figure 3-6 shows the answers from the two questions regarding how often the participants participated in collective action before and after using Weibo. Due to the limited sample in this research, there were only 47 respondents who participated in collective action because they received related information on Weibo. According to the descriptive statistics, only four people participated in boycotts more often after using Weibo. Four people reported they signed petitions more often after using Weibo. Eight people participated in online protest more frequently after using Weibo. Overall, the statistics above show the potential for Weibo to mobilize more people to participate in collective action. Even though there were a limited number of respondents, it suggests that using Weibo might affect people’s collective action involvement. Figure 3 Collective action involvement before and after Weibo use In order to further explore this question, binary logistic models were designed to test the causal relationships of Weibo use patterns and forwarding posts about Weibo activism issues on collective action involvement. The next section presents these findings.
What is the role of Weibo in raising awareness about upcoming collective action? How is collective action involvement related to specific Weibo activism? In other words: do Weibo use patterns and Weibo activism use affects collective action involvement? In order to answer these questions, I must structure the analysis into two sequential steps. In the first step, I use several Weibo use pattern variables to predict involvement in collective action. In the second step, I introduce Weibo activism use variables into the model. The analyses are all binary logistic regression models since dependent variables are mostly in binary measures (0= “No” and 1 = “Yes”), except Weibo use frequencies, which are interval variables. The figures presented for each model are B coefficient, standard errors and odds ratios. A B coefficient figure larger than 1 denotes a positive relation, while a figure smaller than 1 indicates a negative relation. Weibo informational use variables are dichotomous variables with 0 (No) as a baseline. Variables of how often one forwards posts about a series of issues were originally in six-scale, where 0 means never and five means all the time. They were transformed into dichotomous variables in order to add stronger effects into the equation. Original responses of three or above became 1, and all the other responses (two or below) were transformed into 0 (baseline). The baseline variable means one forwards activism issue-related posts rarely, the variable in the equation means one forwards activist posts often. Asterisks and “#” indicate the significance levels. As the sample in this research is relatively small, future studies with larger samples might find the results at p<.1 significance level useful—that is why they are included in the findings. However, the primary focus of this research is analyzing the results at p<.05 level significance.
Table 3 Binary logistic regression of Weibo use frequency, informational Weibo use, and forwarding Weibo activism issues on participating in boycott
Table 3-12 contains the results of binomial logistic regression of Weibo use frequency, informational Weibo use, and forwarding posts about Weibo activism issues on boycott participation. In the first model I only include Weibo use pattern variables—Weibo use frequency and informational Weibo use. As the Hosmer and Lemeshow Test is not significant (p=.928), this indicates that Model 1 improves on the null model (the model without independent variables). However, the Nagelkerke R2 showed these factors statistically explained a small part of the variances in the outcome, with only 6.6 percent of the total equation explained by these factors. The model now correctly classified the outcome for 88.3 percent of cases, which shows no improvement from the null model. Only the variable “Weibo as main source of information” is marginally significant at p<.10. Model 2 adds Weibo activism issue variables into the equation in addition to the variables included in model 1. The Nagelkerke R2 increases from .066 to .189, indicating that Model 2 provides a better fit than the first model. The Hosmer and Lemeshow Test is also not significant (p=.288), meaning the model fits the data well. However, only forwarding Weibo posts about policy related issues is associated with participating in boycotts at p<.10. People who forward Weibo posts about policy related issues more frequently are more likely to join in a boycott. Overall, the Weibo use patterns and Weibo activism use are not particularly good predicators for boycott participation. In other words, Weibo use will not affect involvement in boycotts. Even though in each model there is one variable that is marginally associated with joining a boycott, there is a 10 percent possibility that it is happening by chance.
Table 3 Binary logistic regression of Weibo use frequency, informational Weibo use, forwarding Weibo posts about activism issues on signing a petition
Table 3-12 shows findings about petition signing. The dependent variables in Model 1 and Model 2 are consistent with the model of boycott participation. In general, both Model 1 and Model 2 explain a limited amount of variance in the outcome. The Nagelkerke R2 indicates that only 0.6 percent of total variance was explained by the first model. The second model improves upon the prediction of the previous model to 4.5 percent. There is no dependent variable in the first model that has a significant association with petition signing. In the second model, only forwarding posts related to online charity is associated with petition signing with marginal significance (p<.1). In sum, Weibo use patterns and Weibo activism use will not affect users’ involvement in petition signing.
Table 3 Binary logistic regression of Weibo use frequency, informational Weibo use, forwarding Weibo posts about activism issues on participating in Weibo-driven events
Table 3-13 presents findings about Weibo-driven event participation. The first model shows that use of Weibo to share information with others is positively associated (p<.01) with participation in Weibo-driven events. The Hosmer and Lemeshow Test indicates that the model fits well with the data, eight percent of total variances in outcome can be explained by Model 1 (Nagelkerke R2 =.080). Adding the Weibo activism use variables into Model 2 did improve its predictability compared with the previous model (Nagelkerke R2 increases with .049), indicating that a difference exists between Weibo activists’ and non-activists’ involvement in Weibo-driven events. However, in the new model only forwarding about food safety issues is positively associated with joining Weibo-driven events at p<.1 level. Those who forward posts about food safety issues more often are more likely to join Weibo-driven events. Use of Weibo to share information is still significant in Model 2. In the end, Model 2 correctly classified the outcome for 84.35 percent of the cases compared to 79.90 percent in the null model and Model 1, which is a substantial improvement. The Hosmer and Lameshow Test shows the model is a good fit to the data. An indication of the size of the effects can be seen in the odds ratios (OR). Respondents who use Weibo to share information with others, in other words, to increase their online influence, are three times more likely to join in Weibo-driven events than those who don’t. Meanwhile, people who often forward food safety-related posts are also about three times more likely to participate in Weibo-driven events. After running Spearman’s correlation for the predictors, the result showed no two variables in the equation have a correlation coefficient above .8. So there is no multicollinearity problem in this model. In addition, Cook’s distance shows that no variable was found to be unduly influencing the model. To sum up, use of Weibo to share information with others is associated with participation in Weibo-driven events, confirming the previous hypothesis. Hypothesis 5 is supported in regards to participation in Weibo-driven events, since informational use of Weibo affects involvement in Weibo-driven events. Interestingly, the result shows that forwarding Weibo posts about online charity is not associated with participation in Weibo-driven events. However, forwarding Weibo posts about food safety is associated with involvement with Weibo-driven events at marginal significance (p<.1). This result contradicts a previous assumption: that Weibo-driven events are generally related to online charity, so people who often forward Weibo posts about online charity are more likely to join. On the contrary, it is possible that people who are willing to forward online charity Weibo posts might not be willing to personally engage in the Weibo-driven events.
Table 3 Binary logistic regression of Weibo use frequency, informational Weibo use, forwarding Weibo posts about activism issues on online protest participation
Finally, Table 3-14 reports findings about participation in online protests. In Model 1, the evidence indicates that Weibo use in days per week is positively associated with online protest (p<.05), with more days of Weibo use every week leading to more online protest participation. On the other hand, use of Weibo as a main source of information is also positively associated with online protest at p<.1. As the Nagelkerke R2 shows, the Model explains 14.1 percent of variance in online protest. This figure is higher than Model 1 had explained in the other forms of collective action. Thus, Weibo use frequency and informational Weibo use affect online protest participation. Weibo users who use it more frequently and use it for informational purpose are more likely to join in online protests. When forwarding Weibo posts about various activism issues were added to Model 2, in addition to the variables included in Model 1, one variable appears to be negatively associated with online protest: forwarding Weibo posts about online charity. It indicates that people who forward online charity posts more often are less likely to join in online protests. However, it is significant only at p<.1 level. The other two variables associated with online protest still have a significant association in Model 2. The Nagelkerke R2 increases from .141 to .251, which is a substantial improvement. According to the classification accuracy, the model correctly classified the outcome for 86.6 percent of the cases compared to 82.7 percent in the null model and Model 1. No multicollinearity problem was found after running Spearman’s correlation between the predictors. Cook’s distance showed that no variable was found to be unduly influencing the model. This result contradicts the previous assumption that people who often forward Weibo posts about contentious subjects are more likely to join online protests. These variables do not turn out to be good predictors of involvement in online protests. Considering the results, an interesting conclusion seems to be confirmed: more frequent use of Weibo and use of Weibo for informational purposes predict online protest involvement. However, Weibo activism use failed to predict involvement in online protests. In other words, Weibo activists who participate in online protests are using Weibo more frequently and use it for informational purposes compared with average Weibo users. Conclusion Using survey data, this section tried to understand the causal relationship of Weibo use patterns and Weibo activism use on collective action involvement. The fifth hypothesis (H5) is partially supported. Weibo use can predict involvement in Weibo-driven events and online protest. However, participation in boycotts and online petitions is not affected by Weibo use. That no respondents in our sample ever joined in offline protest within the past year indicates that Weibo has very limited impact in mobilizing offline protest in China.
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