Intellectual dark web, alt-lite and alt-right: Are they really that different? a multi-perspective analysis of the textual content produced by contrarians

Abstract

Contrarian groups, notably Intellectual Dark Web, Alt-lite, and Alt-right, are present across the Web, ranging from fringe websites to mainstream social media. Such massive presence raises major concerns as contrarians often engage in the spread of conspiracy theories and hate speech toward particular groups of people. Historically, there is a general sense that these groups exhibit different degrees of extremism, with Alt-right standing out as the most extremist one. In particular, prior work often takes participation in Alt-right communities as a proxy for radicalization. Yet, to which extent are these groups really different? While most previous analyses have focused on a content consumption (i.e., viewer) standpoint, no prior work analyzed these groups (i.e., contrarians) from a content production perspective. Are there significant differences in the content produced by them? Toward tackling this question, we here analyze the textual data associated with videos shared by the three aforementioned groups. Specifically, we analyze 14 years of content produced by contrarians on YouTube with data from 355,000 videos. Firstly, we assess the degree of toxicity of the content created by each contrarian group, comparing them to one another and, for control purposes, against traditional media content. The results show that all contrarian groups have a more skewed toxicity distribution than traditional media. Yet, all three groups exhibit very similar textual toxicity properties. Further analyses based on psycholinguistic properties and semantic (text) classification reinforce the observation that indeed there is great similarity among the content created by all three contrarian groups. These results suggest that, despite the different definitions, the three contrarian groups are indeed much more similar, in terms of the content produced and shared by them, than the general wisdom (and literature) seems to suggest. Moreover, we also identify a significant temporal increase in content toxicity in all three groups, corroborating prior observations regarding the escalation in the harmfulness of online speech over the years.

A Appendix

This appendix aims to provide additional information on some the techniques used in this paper.

1.1 A.1 Perspective attributes

In this section of the Appendix, we will go into more detail on Perspective API. Perspective API scores text on the impact said text may have on the reader and is widely used in the literature, as mentioned in Sect. 3 (Methodology). Perspective can provide scores for many attributes, and in this work, we used Perspective to infer scores for "toxicity," "severe toxicity," "insult," "profanity," "threat," and "inflammatory." Table 7 displays definitions of each attribute. Although Perspective allows users to use experimental (i.e., not thoroughly tested yet), we pertained to attributes used in production. Further details on additional attributes are available in Perspective’s documentation.⁷

Finally, although Perspective’s implementation is not open-source, their team has released information on how the current system was trained and deployed, including the pretraining of the model (Lees et al. 2022).

Table 7 Perspective’s definitions of analyzed attributes

1.2 A.2 LIWC

The foundation of Linguistic Inquiry and Word Count (LIWC) stems from extensive scientific research spanning decades, showcasing the capacity of language to offer profound insights into individuals’ psychological states, encompassing emotions, cognitive styles, and social concerns. While some connections are straightforward, like the use of positive words indicating happiness, such as "happy," "excited," and "elated," many relationships between verbal expression and psychology are less apparent. For instance, higher social standing and confidence are linked to elevated use of "you" words and reduced use of "me" words. LIWC relies on decades of empirical research and provides specialized means to comprehend, elucidate, and quantify psychological, social, and behavioral phenomena.

LIWC is a text analysis program that analyzes individual or multiple language files quickly and efficiently. It is designed to be transparent and flexible, allowing users to explore word use in various forms. LIWC is used in research to analyze the ways people use words when communicating, which can provide rich information about their beliefs, fears, thinking patterns, social relationships, and personalities. Further details on how LIWC was built are available in its documentation (Pennebaker et al. 2015). The extensive research employed in developing LIWC motivated us to use it in our methodology. In our work, we employed LIWC to analyze each word of an input text automatically, attributing it to a psycholinguistic class. Then, it calculates the overall frequency of each one of its categories in the input text. We relied on the frequency report returned by LIWC for the analyses of our second research question, implementing minor pre-processing, namely the removal of URLs and covert all text inputs to lowercase.

1.3 A.3 Embeddings

For the fine tuning, the first step is the prepare data for training. Given that, the training data for the fine-tuning is very skewed (see Table 1), with the Media category containing the most entries. To avoid learning biases, we employed an under-sampling strategy to build a balanced subsample of the training set prior to the classification analysis. The sampling strategy randomly selects 17k entries from each category based on the size of the smallest category (Alt-right), resulting in 68k entries for fine-tuning. Rather than focusing on the final accuracy of the classifier, which could benefit from more data, our main interest is in evaluating the model’s capability of discriminating among the categories under similar conditions. The model consists of a classification layer over the BERT Tiny pre-trained model,⁸ which is chosen over Vanilla BERT due to resource limitations. The model consists of a classification layer over the BERT Tiny pre-trained model, which has slightly superior classification effectiveness compared to BERT Tiny (Jiao et al. 2020), but has a much higher training cost.

We employ a five-fold cross-validation procedure to assess the classification model’s discriminative capability. Data is split into five partitions, with four used for training and one for testing. The procedure is repeated five times with different training/test partitions, and the reported results are averages over the 5 test partitions. The model is trained for five epochs with 512 as the max input size of tokens, the standard maximum BERT-like model implementations, and a batch size of 16 entries as the maximum allowed due to resource restrictions. Other parameters are the default of the HuggingFace’s trainer,⁹ representing standard values. We use the [CLS] token output to capture contextual embedding representations for all entries of the balanced dataset sample. BERT represents a sentence as a sequence of hidden states, which must be reduced to a single vector for downstream tasks. Therefore, BERT prepends a [CLS] token (short for “classification”) at the beginning of each sentence and uses a more straightforward method of taking the hidden state corresponding to the first token.