Frames and Fields in the Age of the Networked Individual

Some scholars have argued that the role of social movement organisations (SMOs) in the development of collective action frames is being diminished, or at least reevaluated, as the Internet in general, and social networking sites in particular, allow individuals to share personal stories with like-minded people without their intermediation, i.e. personalised action frames (Bennett and Segerberg 2012, Bennett 2012).
This paper investigates whether theoretical frameworks used to study SMOs can be fruitfully adapted to examine the behaviour of so-called networked individuals operating in social media environments. Specifically, we posit that certain Twitter hashtags (for example, those relating to Occupy Wall Street movement, OWS) can be used to demarcate an online activist field.
A field social arena in which participants vie for the definition of the most urgent cause or risk issue or frame. Fields have been used in the context of social movements (McAdam and Scott 2005), and a related research stream is Bourdieu's (1985) conception of society as composed of overlapping fields with their own “rules of the game” where actors exchange diverse forms of power, defined as cultural, economic, social and symbolic “capital” (see also Fligstein & McAdam's (2012) more recent work on “strategic action fields”).
While fields are traditionally considered at the organizational level, if we consider the networked individual an autonomous actor, capable of adapting and negotiating frames without being a part of an SMO, then there is also need for a new type of theory surrounding the concept of a ‘field’. Therefore this paper further proposes that other
Twitter hashtags, used in combination with OWS-related hashtags, may be seen as emergent collective action frames.
Using an OWS Twitter dataset collected between October 2011 and February 2013, we test whether certain hashtags can be understood as collective action frames by analysing the response of actors on the field to the emergence of these hashtags. The hashtags: #ows and #occupywallstreet, were chosen as the basis for the sample (and thus delineates the field). The hashtags were chosen as they refer to the OWS-movement as a whole, and not to any of the sub-frames within the movement. The data used was collected on 141 spearate occations, divided between the two hashtags as follows:
• #occupywallstreet: data collected on 74 sepatate occasions between the time period: 12.10.2011 and 07.02.2013.
• #ows: 67 sepatate occations between the time period: 21.11.2011 and 07.02.2013.
The hashtag #s171 was chosen as it both represents a clear connection to the OWSmovement, and is new within the timeframe of the available data. This was important, as the goal of this study is to explore how different members of a social movement respond to a new a collective action frame (within the field). Hashtag s17 emerged in the field (#ows and/ or #occupywallstreet network) on 5th of March 2012.
In previous work on online environmental activists, O'Neil and Ackalnd (2014) have provided empirical support for a central tenet of field theory, namely that challengers are more likely to adopt an emergent frame as a means of gaining influence and recognition.
In the present paper we test this with Twitter data by using logistic regression to evaluate the response of different types of actors to emergent frames.
A logistic regression analysis (table 1, next page) was conducted with the dependent variable answering the question: After being exposed to the new collective action frame (#s17) did the actor use #s17 in their first tweet after the exposure2? (0= No and 1= Yes).
For the variable ‘Days on Occupy’3 the regression analysis shows that for each extra day the since the actor first became active on the occupy hashtags, the odds of the first tweet after exposure (to the #s17), including the hashtag s17, are reduced by 0.03% (p < .05).
One might think that the negative correlation for ‘Days on Occupy’ is due to old members having stopped tweeting on Occupy related topics, (i.e. loss of interest in the movement). However, to be included in the sample the actors have to have tweeted on either: #ows or #occupywallstreet, or on the #s17, after being exposed to #s17. The correlation expressed in the regression analysis is not the result of the ‘older’ actors having stopped being involved in the Occupy movement, but rather an expression of different interest among new and old members.
‘Number of Occupy Related Tweets’4, is also significant and shows that actors who have tweeted between 3-12 times on the Occupy related hashtags, are 64% less likely to tweet using the #s17 (p < .001), than actors who have tweeted on the two occupy related hashtags less than 3 times (reference category). Similarly actors who have tweeted 13 times or more on the occupy related hashtags are 99% less likely to use the s17 hashtag in their first tweet after exposure (p < .05), when compared to actors who have tweeted on the two occupy related hashtags less than 3 times.
In other words, we have two measures of how well established an actor is within the field, as we are measuring both the length of involvement (Days on Occupy), and the level of involvement (Number of Occupy Related Tweets), and both measures show a negative correlation with adopting the new frame: #s17.
This result matches O'Neil and Ackland's (2014) previous research on SMOs, thus supporting the argument that the networked individual, like SMOs, can be thought of as an autonomous actor within a field.


FULL VERSION Frames and Fields in the Age of the Networked Individual