A computational text analysis investigation of the relation between personal and linguistic agency

Previous psycholinguistic findings showed that linguistic framing – such as the use of passive voice - influences the level of agency attributed to other people. To investigate whether passive voice use relates to people’s personal sense of agency, we conducted three studies in which we analyzed existing experimental and observational data. In Study 1 (N = 835) we show that sense of personal agency, operationalized between participants as recalling instances of having more or less power over others, affects the use of agentive language. In Study 2 (N = 2.7 M) we show that increased personal agency (operationalized as one’s social media followership) is associated with more agentive language. In Study 3 and its two replications (N = 43,140) we demonstrate using Reddit data that the language of individuals who post on the r/depression subreddit is less agentive. Together, these findings advance our understanding of the nuanced relationship between personal and linguistic agency.


Supplementary Methods
Messages on Twitter differ in their target audience.Top-level messages (i.e., "posts") target the entire social network of a single user.However, a conversation produces lowerlevels messages (i.e., "replies") that usually come in response to a post or to another comment in the conversational chain.Is there a difference in the relationship between passive voice and followership, as a function of message level?
To examine the relationship between passive voice usage and message level, we used the same random sample from the CCR analyses with a total of N = 100,000 posts.After excluding multiple posts by individual users, the sample size was reduced to N = 81,606.In our analysis, we investigated how the use of passive voice varied based on the message level (i.e., top-level vs. replies), controlling for tweet length (median center).We found that the main effect of passive voice remained significant and exhibited the same direction (IRR = 0.82, 95% CI [0.77, 0.88], p < .001)and there was a main effect of tweet length (IRR = 1.05, 95% CI [1.05, 1.05], p < .001).Additionally, we observed a main effect of message level, indicating that replies were associated with users who had fewer followers (IRR = 0.50, 95% CI [0.48, 0.51], p < .001).However, we did not find evidence of an interaction between passive voice usage and response level (IRR = 0.93, 95% CI [0.84, 1.03], p = 0.17).

Annotation instructions:
We are interested in non-agentive language.
Agentive language refers to the case where a sentence has a specified agent.Someone who is responsible for the event.For example: Adam kicked the ball.In This case, Adam is the agent as he kicked the ball.In the sentence "the ball was kicked" or "the ball got kicked" there is no agent -the ball was just "kicked" by an unknown person.However, when a passive sentence followed by a "by" phrase (e.g., "the ball got kicked by Adam") it is considered an agentive sentence.Therefore, not all passive sentences are considered non-agentive.
More examples for non-agentive sentences: "The vase broke" (vs."John broke the vase") "The book was put on the table" (vs."Michelle put the book on the table") "The curtain caught fire" (vs."I set the curtain on fire") Your job is to identify instances of non-agentive language and count them in the given texts.For each text, please write down the number of non-agentive instances in the associated text.

Examples:
1) "Michael went to get some beer.He drove his car into the nearest "Shufersal" and parked his car.The car bumped into an e-scooter, knocking over the e-scooter driver who had to be taken to nearest hospital" (count = 2) 2) "Michael apologized to the e-scooter driver and promised to pay his medical bills" (count = 0)  Table S1.Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a function of power condition (high vs. low) and self-referential language (I-words) in Study 1. Self-referential language and word count are median-centered.

Figure S1 .
Figure S1.Histogram of passive auxiliary verbs in Study 1.

Table S2 .
Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a function of power condition (high vs. low) and self-referential language (I-words) in Study 1, controlling for gender.Self-referential language and word count are median-centered.

Table S3 .
Negative Binomial Generalized Linear Model predicting number of I-words (selfreferential language) as a function of number of group condition in Study 1. Word count are median-centered.

Table S4 .
Negative Binomial Generalized Linear Model predicting number of followers as a function of number of passive auxiliary verbs in tweet and self-referential language (I-words) in Study 2. Self-referential language and word count are median-centered.

Table S5 .
Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a function of online community (depression vs. random sample of popular communities) and self-referential language (I-words) in Study 3a.Self-referential language and word count are median-centered.

Table S6 .
Negative Binomial Generalized Linear Model predicting I-words as a function of online community (depression vs. random sample of popular communities) in Study 3a.Word count is median-centered.

Table S7 .
Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a function of online community (depression vs. random sample of popular communities) and self-referential language (I-words) in Study 3b.Self-referential language and word count are median-centered.

Table S8 .
Negative Binomial Generalized Linear Model predicting I-words as a function of online community (depression vs. random sample of popular communities) in Study 3b.Word count is median-centered.

Table S9 .
Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a function of online community (depression, support groups and a random sample of popular communities) and self-referential language (I-words) in Study 3c.Self-referential language and word count are median-centered.

Table S10 .
Negative Binomial Generalized Linear Model predicting I-words a function of online community (depression, support groups and a random sample of popular communities) in Study 3c.Word count are median-centered.

Table S11 .
Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a function of online community (depression vs. random sample of popular communities) in Study 3a (following pre-registration).Word count is median-centered.

Table S12 .
Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a function of online community (depression vs. random sample of popular communities) in Study 3b (following pre-registration).Word count is median-centered.