Impact of Stricter Content Moderation on Parler’s Users’ Discourse

Social media has become a powerful tool that reflects the best and worst aspects of human communication. On one hand, they allow individuals to freely express opinions, engage in interpersonal communication, and learn about new trends and stories. On the other hand, they have also become fertile grounds for several forms of abuse, harassment, and the dissemination of misinformation (Micro, 2017; Khan et al., 2019; Anderson et al., 2016; Singhal et al., 2023a). Social media platforms, hence, continue to adopt and evolve their content moderation techniques and policies to address these issues while trying to respect freedom of speech and promote a healthier online environment.

Social media platforms, however, do not follow unified methods and policies for content moderation (Singhal et al., 2023b). While some social media platforms adopt more stringent content moderation rules, others, like Parler, pursue a laissez-faire approach. Parler, launched in 2018, adhered to this hands-off moderation philosophy, contending that it promoted richer discussions and protected users’ freedom of speech (Rothschild, 2021). This was until January 6th, 2021, when Parler gained much notoriety for being home to several groups and protesters who stormed Capitol Hill (Groeger et al., 2017; Rondeaux et al., 2022). Subsequently, due to its content moderation policies and concerns about the spread of harmful or extremist content, Parler faced significant consequences. It was not only terminated by its cloud service provider, Amazon Web Services but also removed from major app distribution platforms, including the App Store and the Google Play store (Fung, 2021).

For Paler to return to the Apple App Store, it had to enact substantial revisions to its hate speech policies.¹¹1Example of changes in Parler CG: https://tinyurl.com/yda6pfmj This included a complete removal of the ability for users on iOS devices to access objectionable and Not Safe for Work (NSFW) content. As a result, Parler’s updated policies introduced more stringent moderation policies aimed at curbing hate speech on the platform (Lerman, 2021).

While several other prior studies have focused on the impact of de-platforming users or certain communities (Rogers, 2020; Rauchfleisch and Kaiser, 2021; Ali et al., 2021; Jhaver et al., 2021; Ribeiro et al., 2020), or investigated how content moderation has an impact on activities of problematic users (Trujillo and Cresci, 2022; Ali et al., 2021), our work is the first that investigates the impact of stricter content moderation policies on the platform’s content and userbase. In particular, we investigated two research questions: RQ1: What are the immediate and long-term effects of Parler’s content moderation revisions on the user-generated content? RQ2: How have Parler’s content moderation revisions changed its user base?

The immediate effects of Parler’s content moderation revisions on its user-generated content may include reduced hate speech, changes in user behavior, and user base shift. Stricter content moderation policies are likely to lead to an immediate reduction in hate speech and other objectionable content on the platform. Users posting such content may find their posts removed or their accounts suspended. Users may also quickly adapt to the new moderation policies by altering their behavior. They might refrain from posting controversial content to avoid sanctions. Also, some users may leave the platform in response to the changes, while others may join because they find the environment more welcoming. These shifts can occur relatively quickly. In our study, we could investigate the first two immediate effects of Parler content moderation changes. However, we did not examine user base shift due to lack of data and its ethical issues.

The long-term effects of Parler’s content moderation revisions on its user-generated content may include user base transformation, and content quality, and User base transformation, when over time, the platform’s user base may shift in terms of its demographic, values, and the types of content posted. Content quality indicates when the overall quality of content on the platform may improve. Users may engage in more civil and constructive discussions, which can attract a different user base. To assess user base transformation and content quality, we conducted a quasi-experimental time series analysis, monitoring user posts for toxic content, insults, identity attacks, profanity, and threats. In addition, we explored shifts in conversation topics and quantified the presence of biased posts and posts with non-factual links, utilizing data sourced from Media Bias Fact Check (MBFC) (mbf, 2022a).

To answer the above research questions, we used the data from Aliapoulios et al. (Aliapoulios et al., 2021a) as the seed dataset (we call this dataset a pre-moderation change dataset), and we tried to collect the posts for the same sample of users (i.e., 4M). Using our custom build crawler, we could collect about 17M parleys of 432K active users from February 2021 to January 2022. We labeled our dataset as a post-moderation policy change dataset. To the best of our knowledge, ours is the first dataset that was collected after Parler came back online. We will make our dataset available to the public. To measure the immediate and long-term effect of Parler’s content moderation changes, we used the Interrupted Time Series (ITS) regression analysis, which is arguably one of the strongest and widely used quasi-experimental methods in causal inference (Bernal et al., 2017; Horta Ribeiro et al., 2023; Trujillo and Cresci, 2022; Jhaver et al., 2021). This analysis helped us understand how and if the outcomes, e.g., the number of toxic posts, have changed after this intervention and if the effects have been sustained over time.

Thus, in this paper, we have the following contributions and findings:

1. (1)

   Our work shows how content moderation effectiveness can be tested employing data-driven analysis on data obtained from the platform (here Parler).

2. (2)

   We collected Parler data after its return to the Internet and App Store, hence, the first-ever post-de-platforming dataset.

3. (3)

   Using a quasi-experimental approach, we found that Parler was effective in removing the most severe forms of toxicity and profanity. We also found that Parler users’ identity attacks increased just after Parler came online (0.5 – 0.8). However, in the long-term, Parler was effective in decreasing identity attacks.

4. (4)

   Our findings showed an increase in both follower and following counts, as well as an uptick in users with verified and gold badges. This suggests the potential growth of Parler’s user base and the continued presence of older users who were active before the moderation policy changes.

5. (5)

   We observed an improvement in factuality and credibility scores from the pre-moderation dataset to the post-moderation dataset. Additionally, we noted a reduction in the sharing of conspiracy and pseudoscience source links. However, there was an increase in the sharing of questionable source links in the post-moderation change dataset.

Fringe Communities: Over the past few years, scholarships have extensively studied various fringe platforms such as Gab, Voat, 4chan (Papasavva et al., 2020a; Hine et al., 2017; Bernstein et al., 2011; Tuters and Hagen, 2020; Zannettou et al., 2018; Sipka et al., 2022; Israeli and Tsur, 2022; Jasser et al., 2023). When compared to other fringe social media, Parler is younger. Due to this, we notice that not a lot of studies have focused on collecting or establishing a framework to collect data from Parler (Aliapoulios et al., 2021a; donk_enby, 2021; Prabhu et al., 2021). There have been studies comparing topics of discussion on Parler and Twitter (Prabhu et al., 2021; Sipka et al., 2022; Jakubik et al., 2022). Although there exists work in this domain, most of the work is focused on exploring the existence or prevalence of a single topic. Hitkul et al. (Prabhu et al., 2021) uses the capitol riots, a pivotal movement in Parler’s history, to compare topics of discussion between Parler and Twitter. Works have analysed the language in Parler in several aspects such as QAnon content (Sipka et al., 2022; Bär et al., 2023; Jakubik et al., 2023), and COVID-19 vaccines (Baines et al., 2021). We believe that our work differs in this aspect as we are studying the changes localized to Parler and how users reacted to a brief hiatus of Parler.

Studies about Deplatforming: Jhaver et al. (Jhaver et al., 2021) examined how deplatforming users on Twitter could impact their userbase. They found that banning significantly reduced the number of conversations about all three individuals on Twitter and the toxicity levels of supporters declined. Trujillo & Cresci (Trujillo and Cresci, 2022) found that interventions had strong positive effects on reducing the activity of problematic users both inside and outside of r/The_Donald. Some scholarships have examined the effects of de-platforming individuals on the sites that sanctioned influencers move to post-deplatforming (Ali et al., 2021; Rauchfleisch and Kaiser, 2021; Rogers, 2020; Newell et al., 2016; Ribeiro et al., 2020; Russo et al., 2023; Horta Ribeiro et al., 2023). These researchers found a common result, that de-platforming significantly decreased the reach of the de-platformed users, however, the hateful and toxic rhetoric increased. Unlike previous studies, instead of exploring how users’ discourse changes when switching to different platforms, we examine the same users’ discourse when the platform undergoes stricter content moderation policies.

Hate Speech Detection and Classification: Empirical work on toxicity has employed machine learning based detection algorithms to identify and classify offensive language, hate speech, and cyberbully (Zhang et al., 2016; Davidson et al., 2017; Koratana and Hu, 2018; Pitsilis et al., 2018). The machine learning methods use a variety of features, including lexical properties, such as n-gram features (Nobata et al., 2016), character n-gram features (Mehdad and Tetreault, 2016), character n-gram, demographic and geographic features (Waseem and Hovy, 2016), sentiment scores (Dinakar et al., 2012; Sood et al., 2012; Gitari et al., 2015) average word and paragraph embeddings (Nobata et al., 2016; Djuric et al., 2015), and linguistic, psychological, and effective features inferred using an open vocabulary approach (ElSherief et al., 2018). The state-of-the-art toxicity detection tool is available through Google’s Perspective API (Google Perspective API, 2020). Perspective API has been extensively used in the previous studies (Kumarswamy et al., 2022; Salehabadi et al., 2022; Zannettou et al., 2020; Gröndahl et al., 2018; ElSherief et al., 2018; Saveski et al., 2021; Papasavva et al., 2020b; Aleksandric et al., 2022; Hickey et al., 2023).

Media Bias Fact Check: Gruppi et al. (Gruppi et al., 2021b) used MBFC service to label websites and the tweets pertaining to COVID-19 and 2020 Presidential elections embedded inside these articles. Weld et al. (Weld et al., 2021) analyzed more than 550 million links spanning 4 years on Reddit using MBFC. MBFC is widely used for labeling credibility and factuality of news sources for downstream analysis (Main, 2018; Heydari et al., 2019; Starbird, 2017; Darwish et al., 2017; Nelimarkka et al., 2018; Etta et al., 2022; Bovet and Makse, 2019; Cinelli et al., 2020, 2021; Weld et al., 2021) and as ground truth for prediction tasks (Dinkov et al., 2019; Stefanov et al., 2020; Patricia Aires et al., 2019; Bozarth et al., 2020; Gruppi et al., 2021a).

We will now describe in detail about Parler and also our data collection methodology.

Parler: Parler was a microblogging website that was launched in August 2018. Parler marketed itself as being “built upon a foundation of respect for privacy and personal data, free speech, free markets, and ethical, transparent corporate policy” (Herbert, 2020). Parler is known for its minimal restrictions and many right-wing individuals, citing censorship from mainstream platforms joined Parler (Singhal et al., 2023b; con, 2020). However, after linking the 2021 US Capitol Riots and the activity of some groups on Parler, such as QAnon and Proud Boys, on January 12, 2021, Parler was removed from the Apple App Store, the Google Play Store, and Amazon Web Services (Fung, 2021). Parler had to change considerably its hate speech policy to return to the Apple App Store (Lerman, 2021). Parler came back online with a new cloud service provider in early February 2021 (Roberston, 2021). On April 14th, 2023, Parler was bought a digital media conglomerate Starboard, and it has been offline after that (Weatherbed, 2023).

Pre Policy Change Dataset: The data collection tool used in Aliapoulios et al. (Aliapoulios et al., 2021a) managed to collect user information for almost all of the users present at the time based on estimates published by Parler. They also managed to collect posts and comments from these users dating back to 2018 when Parler was created. The study collected user information from more than 13.25M users’ and randomly selected 4M users, from which they obtained about 99M posts (or parleys), and about 85M comments from August 1st, 2018 to January 11, 2021. In our study, we call this dataset the pre-moderation change dataset. We used the 4M users as the seed dataset to collect data post-moderation changes instituted by Parler.

Post Policy Change Parler Dataset: Parler started to employ a different mechanism for authenticating API requests in February of 2021. Due to these changes, we could no longer collect data from all users. Hence, we first obtained the list of 4M users which is a random set from 13.25M users provided in the pre-moderation change dataset (Aliapoulios et al., 2021b) and used our custom-build framework to get the content posted by the same users. Using this framework, we collected information about the post body, any URLs posted, a URL to the location of any media posted, the date posted, the number of echoes, and other metadata, such as the badges of the poster. Authors in (Aliapoulios et al., 2021a) obtained the metadata of 13.25M users, hence we also tried to collect the metadata of these users.

Post Policy Change Dataset Statistics: From the 4M users, we could collect 17,389,610 parleys from 432,654 active users. Our dataset consists of parleys from February 1st, 2021 to January 15th, 2022. We used the /pages/feed endpoint, which returns the parleys (posts) posted by a specific user using their username. Note that, this endpoint is different from the endpoint that is used to collect the 13.25M users’ metadata, and hence we were only able to obtain 432K users’ parleys. Several users from the initial seed dataset of 4M were no longer active or had deleted their accounts, or changed their usernames. We label them as missing users. Even though we are unsure if these users were suspended by Parler or they decided to leave Parler, we nevertheless analyzed and compared these users with those that remained active. Since we only collected posts from 432,654 users, we acknowledge that certain trends and analyses conducted might not be accurately reflected on the platform. However, as of January 2022, months after returning to the Apple app store, Parler disclosed that they estimate to have around 700,000 to 1M active users (par, 2022). This ensures that we have collected a significant part of the data to study and base our findings on.

These parleys (17M) consisted of users posting around 9M links and plain text in the body. A majority of these posts were primary posts that had no parent. If a parley is an original parley and is not an echo of another parley, it is known as a primary post with no parent. We collected the full-text body, a URL if a link was shared, the title of the parley, the date of creation, flags for trolling, sensitive and self-reported, an upvoted flag, a counter of echoes and likes. We noticed that Parler has a trolling flag, which might be set manually by moderators or automatically by the platform. We also tried to collect profile information for 13.25M users from the pre-policy change dataset. We used the pages/profile/view endpoint, that returns the metadata of the user. We found that 12,497,131 of these users still had a valid Parler account, so we could collect metadata for these users. For a vast majority of the accounts, Parler returned the number of followers, the number of following, status (account available or deleted), the number of and types of badges given to the user, a description of all badges available on Parler at the time of collection, date of parley creation, whether the account is private or public, and also whether the account is being followed by or a follower of the user logged in. A minority of profiles have one or more of these fields missing due to changes on the Parler platform from when the user created the account and the time of data collection.

Twitter Data Collection (Baseline): Major and contentious events, such as the U.S. presidential election, can influence the toxicity in any social media platform. Since our statistical analysis cannot account for such confounding factors, we hypothesize that similar trends may be observable on other social media platforms like Twitter, which did not implement policy changes during the same period. If we observe different trends or varying effect levels, we can be more confident that the shifts in outcomes are attributable to Parler’s content moderation adjustments. For a comparative baseline, we gathered a sample of Twitter data from the same timeframe and analyzed the trends before and after Parler’s policy changes. To collect the data, we used Twitter V2 API (Twitter, 2022) and collected posts daily using the exact timeline of the datasets. To circumvent the API restrictions, we collected 27K posts daily and restricted the posts to English. After the data collection was done, we were able to collect 24.16M posts for a pre-policy timeline and 9.69M for a post-policy timeline.

Ethical consideration. We only gathered posts from Parler profiles set to public and did not attempt to access private accounts. We also made no attempts to gather this data maliciously. Instead, we used the same backend APIs that a user browser would request data from. We only obtained the random sample from Twitter API and did not collect any metadata information about the users’ whose profile were set as private. We also follow standard ethical guidelines (Rivers and Lewis, 2014), not attempting to track users across sites or deanonymize them.

We now describe how we operationalized users’ behavior and characteristics to answer our four research questions.

Measuring Toxicity Scores. To understand the differences in the user discourse in the pre-moderation dataset vs. in the post-moderation dataset, we utilized the Google Perspective API, which is a state-of-the-art toxicity detection tool (Google Perspective API, 2020). This AI-based tool investigates the provided text and assigns a score between 0 to 1, with a higher score indicating more severity for a particular attribute. We obtained the following attributes:

For our study, we collected the likelihood scores for each attribute. While collecting the scores, English was used as the default language for all posts since previous studies showed us that a large majority of Parler’s userbase was using English as their language of choice to communicate with other Parler users (Aliapoulios et al., 2021a). Before sending the posts to Perspective API, we pre-processed the posts by removing URLs, hashtags, etc. as these can lead to wrong scores or errors computing the scores by the API. We also pre-processed our Twitter dataset (Baseline) and obtained the scores via Perspective API. While we acknowledge that Perspective API has weaknesses in detecting toxicity (Hosseini et al., 2017; Welbl et al., 2021), however, prior works have found that it is successful in detecting forms of toxic language in the generated text (Ovalle et al., 2023), and also multiple prior works have used Perspective API for toxicity detection (Aleksandric et al., 2022; ElSherief et al., 2018; Saveski et al., 2021; Hickey et al., 2023), hence it is reasonable to use the API for our study.

Assessing Bias and Factuality: We analyzed the links that were shared on Parler using the Media Bias Fact Check (MBFC) service (mbf, 2022a). MBFC is an independent organization that uses volunteer and paid contributors to rate and store information about news websites (mbf, 2022a). MBFC can be used to measure the factuality of the URL, the presence of any bias, the country of origin, and the presence of conspiracy or pseudoscience, questionable sources, and pro-science sources. We used a list of links shared from both of our datasets to obtain labels for:

Casual Inference: We employed a quasi-experimental approach to measure the effect of Parler’s content moderation change. We leveraged a causal inference strategy called Interrupted Time Series (ITS) Regression Analysis (Bernal et al., 2017). In an ITS analysis, we track the dependent variable, i.e., toxicity attributes over time and use regression to determine if a treatment at a specific time (i.e., Changes in Parler’s content moderation policies after its return) caused a significant change in the toxicity attributes. The ITS regression models the behavior of the pre-treatment time series to predict how the series would have looked if the treatment had not been applied (Jhaver et al., 2021). It is important to regress the behavior on time; otherwise, we could easily misinterpret a steadily increasing (or decreasing) time series as a treatment effect when comparing the averages of behavior prior to and post treatment (Bernal et al., 2017). Hence, ITS analysis allows us to claim that the toxicity changed at the time of de-platforming, instead of simply reflecting a general trend. Herein we employ a linear model and a separate logistic regression model for analyzing users’ rhetoric, based on different thresholds of the perspective scores:

where $Y_{t}$ is the toxicity score; T is a continuous variable which indicates the time in days passed from the start of the observational period; D is a dummy variable indicating observation collected before (=0) or after (=1) de-platforming; P is a continuous variable indicating time passed since the intervention has occurred (before intervention has occurred P is equal to 0). $\beta_{1}$ indicated the trend before de-platforming, $\beta_{2}$ indicates the immediate change upon de-platforming, and $\beta_{3}$ indicates the sustained effect after de-platforming.

In our current dataset, i.e., the post-moderation change dataset, we were not able to collect a random sample of users, hence our analysis might not yield a full-scale impact of the moderation policy change. The other limitation of our work is that users might have changed their usernames when Parler was reinstated back online, as to evade possible detection. We also did not run any analysis for the comments on these posts. Comments can also shed light on the moderation changes that Parler undertook.

In the future, we plan to study the user comments on posts to understand if the moderation changes are being reflected in the comments also, as comments can also shed light on the moderation changes that Parler undertook. We also plan to use other toxicity detection methods such as GPT-4 to detect toxicity of users’ parleys.

On January 12, 2021, Parler a social media platform popular among conservative users was removed from Apple and Google App Stores and Amazon Web Services, stopped hosting Parler content shortly after. This was blamed on Parler’s refusal to remove posts inciting violence following the 2021 US Capitol Riots. Parler was eventually allowed back after they promised that they will strengthen their moderation to remove hateful content. Our study looks into the effect of these policy changes on the user discourse in the pre policy dataset that was collected is published in previous studies, and the post policy dataset, which we collected by first establishing a new framework to collect posts and user information from Parler.

Our quasi-experimental time series analysis indicates that after the change in Parler’s moderation, the severe forms of toxicity (above a threshold of 0.5) immediately decreased and sustained. In contrast, the trend did not change for less severe threats and insults (a threshold between 0.5 - 0.7). Finally, we found an increase in the factuality of the news sites being shared, as well as a decrease in the number of conspiracy or pseudoscience sources being shared.