Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity (2022)

Abstract: The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a minimally supervised method that leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of salience and framing, drawing upon insights from moral psychology. Our architecture combines graph neural networks with structured sparsity learning and results in representations for concepts and subreddits that capture temporal ideological dynamics such as right-wing and left-wing radicalization.

## introduction the polarization of online political discourse on platforms such as twitter @xcite , facebook @xcite , and reddit @xcite has received increasing attention in the computational social sciences recently, particularly in the context of covid-19 @xcite . in nlp, a growing body of work has discovered mechanisms by which polarization manifests itself linguistically (e.g., @xcite . however, the methods proposed so far rely on knowing in advance the political orientation of text, a requirement seldom met in social media. in this paper, we propose slap4slip (sparse language properties for social link prediction), a novel framework that fully dispenses with the need for labels and instead leverages the ubiquitous network structure of online discussion forums to detect polarized concepts, making it more scalable and lightweight than previous methods. for example, slap4slip finds that fascist and mainstream are among the most polarized concepts in reddit in 2019 (figure 1 ). we model the polarization of concepts along the dimensions of salience and framing. for framing, we take into account insights about the moral foundations of ideology @xcite and use contextualized bert embeddings to construct subspaces that capture nuanced biases in the way concepts are discussed. contributions. we introduce slap4slip, a framework to detect polarized concepts without information about the political orientation of text. the specific model we propose for slap4slip combines graph neural networks with structured sparsity learning and identifies in a minimally supervised way (i) which concepts are the most polarized ones, (ii) whether the polarization is due to differences in salience or framing, and (iii) which moral foundations are involved (when framing is relevant). drawing on english reddit data, we evaluate the model intrinsically by conducting various experiments and extrinsically by using the found polarized concepts to predict the ideological leaning of us states. the model also learns subreddit embeddings that capture temporal dynamics. 1 key term explanation polarization by polarization we mean the clustering (of nodes, embeddings, etc.) according to ideology. like @xcite , we understand it as a non-binary property, i.e., there can be more than two poles. a concept polarized in salience could be a unigram whose relative frequency has two clusters corresponding to liberalism and conservativism. ## related work our study is closely related to previous nlp work on polarization @xcite @xcite @xcite ), but we try to avoid the need for explicit information about ideologies (e.g., manual labels) by leveraging the network structure of online discussion forums. besides being more readily applicable in practice, this means our method is not restricted to a small number of opposing ideologies, making it theoretically more sound @xcite . there is also work in the computational social sciences showing that the structure of various types of online social networks reflects polarization @xcite @xcite , which has been explained as a result of homophily, i.e., nodes close to each other are likely to share similar views @xcite . while these studies partition the network into a small number of ideological communities, our method does not require a discretization step. more broadly, our study is related to nlp work on ideology in general @xcite @xcite . research in the political sciences has discovered salience and framing as two key dimensions along which the discussion of issues can vary ideologically. salience refers to the amount of importance attached to an issue by individuals @xcite . mass media can impact salience, an effect called agenda setting @xcite . framing refers to the mechanism by which certain aspects of an issue are highlighted @xcite . crucially, framing is different from sentiment: it reflects what considerations are perceived as important, not what stance is taken regarding these considerations @xcite . both salience (with a focus on agenda setting) and framing have been the subject of previous work in nlp @xcite @xcite . here, we use them to characterize differences between online groups. psychological research has shown that the fundamental divisions between different ideologies are rooted in their views of morality @xcite . in moral foundations theory @xcite , this has been formalized as variation along the moral foundations of care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and sanctity/degradation. several studies have shown that moral foundations theory is a suitable basis for analyzing ideological framing @xcite @xcite . we follow this approach, but as opposed to prior work we operate with contextualized embeddings that we project into moral embedding subspaces. methodologically, we draw on advances in deep learning with graph neural networks, specifically graph auto-encoders @xcite . in nlp, such graph-based architectures are increasingly used to include information from social networks for downstream tasks (e.g., @xcite . our work differs in that we combine deep learning on graphs with structured sparsity, a form of regularization similar to ℓ 1 regularization @xcite that sets entire groups of parameters to zero @xcite . structured sparsity has been used in nlp before @xcite @xcite , but not in connection with graph neural networks. the precise definition of the key technical terms in this paper somewhat varies in the literature (e.g., @xcite . ## slap4slip framework the key idea of this paper is to directly leverage the social network structure for determining polarized concepts. 2 we introduce a novel framework called slap4slip (sparse language properties for social link prediction) whose goal it is to model the structure of social networks in a data-driven way that obviates the need for extensive human annotation or partitioning the network into communities. slap4slip is a general framework to detect the most salient types of linguistic variablity in social networks and is in principle applicable in any scenario involving social networks with textual data attached to each node. in this paper, we show that for polarized online discussion forums, slap4slip can be used to find polarized concepts. let @xmath0, e) be a network consisting of a set of nodes v representing social entities and a set of edges e representing connections between the social entities. we denote with @xmath1. let c be a set of word n-grams denoting concepts (e.g., political issues like gun control). here, we confine ourselves to subreddits for v and unigrams and bigrams for c, but slap4slip is applicable in other scenarios (e.g., for networks of people or concepts extracted from text in a more complex manner). we define a function ψ l : v × @xmath2. ψ l can be represented as a matrix in r |v|×|c| , as an example, consider the network in figure 2 . the network consists of two connected components of four edges each, with no edges between the components. c consists of the two concepts c 1 and c 2 . taking the frequency count as linguistic property l and displaying it with the color of nodes, ψ l results in the two signals shown in figure 2 . we can see that the signal of concept c 1 alone allows for a perfect prediction of the network structure according to the decision rule since c 2 cannot achieve a perfect prediction, c * = {c 1 } is the optimal solution. notice the variance of ψ l (v i , c j ) is identical for both concepts and does not represent a good distinguishing factor. notice also that the optimal solution is not necessarily unique: there might be another concept c 3 with a similar frequency count distribution as c 1 such that c * = {c 3 } would also be an optimal solution. ## model we draw upon reddit politosphere @xcite , a pseudonymized dataset based on reddit covering 605 political subreddits (e.g., politics) from 2008 to 2019. 4 for each year, reddit politosphere contains (i) all comments made to the subreddits and (ii) an unweighted graph with the subreddits as nodes and edges computed by applying statistical backboning to the counts of users shared between subreddits. subreddits that have disproportionately many users in common are likely to be ideologically similar @xcite . to ensure robust training, we only use years in which the graph has at least 100 nodes (2013 to 2019). see appendix a.1 for summary statistics. the high modularity values indicate that the graphs are polarized @xcite . we propose a neural architecture that uses information about concept-level salience and framing to predict links between subreddits while reducing the number of considered concepts as far as possible. since the links reflect ideological similarity, this should result in a compact set of concepts that is maximally informative about ideology. the performance on link prediction makes it straightforward to compare the quality of different models. determining concepts. to obtain the concepts c, we create for each year unigram and bigram vocabularies of political comments taken from reddit politosphere and non-political comments sampled in equal size from the default subreddits. 5 to eliminate unigrams and bigrams typical of discussions but not relevant to salience and framing (e.g., dont think), we only consider unigrams and bigrams that appear more often within than outside of noun phrases as detected by a noun phrase chunker @xcite . based on their frequencies within the political and non-political comments, we compute mutual information scores for all unigrams and bigrams and take the top 1,000 unigrams and bigrams for c. this and all other steps are done separately for each year, i.e., we extract year-wise concepts and train year-wise models. modeling salience and framing. the first part of the architecture models ψ l , i.e., it extracts linguistic information related to salience and framing from the subreddits and maps them to scalar representations. in the resulting matrix ψ l , each column is a signal on the entire graph defined by one concept, and each row is a vector for one subreddit defined by all concepts in c (section 3). to model ideologically-driven framing, we use bert @xcite and obtain average contextualized embeddings e(v i , c j ) for each subreddit v i and concept c j . furthermore, we use the moral foundations dictionary @xcite and obtain for each moral foundation m k (e.g., authority/subversion) average contextualized embeddings for the 10 highest-ranked words of both poles. 6 similar to @xcite , we perform pca on the 20 average contextualized embeddings for each m k and use the first principal component as the subspace representation e(m k ). this allows us to project the subreddit-specific average contextualized concept embeddings e(v i , c j ) into the five moral subspaces, salience and framing can be of different importance for different concepts, i.e., there might be concepts with identical values of s(v i , c j ) across all subreddits but maximally polarized values of f (v i , c j ) (or vice versa). to capture this, we combine s(v i , c j ) and f (v i , c j ) in a weighted sum, graph neural network. to predict the links in g, we use a graph neural network @xcite , specifically a graph auto-encoder @xcite , which takes as input the matrix ψ l as well as g's adjacency matrix a. the encoder consists of a two-layer graph convolutional network @xcite . in each layer, the subreddit representations h (d) are updated according to the propagation rule in the decoder, we compute the reconstructed adjacency matrix, â, according to â = σ zz , where we use the sigmoid for σ. â is then used to compute a prediction loss, l (pred) . ## limitations the success of our method depends on how accurately polarization is reflected by the network, which means that care must be taken during network selection (explicit networks) and construction (implicit networks). for example, user overlap on reddit can also be due to conflict between subreddits @xcite @xcite . while we do not find this to affect our results, it might be a limitation if the degree of homophily in the network is too low. this paper only applies slap4slip to networks with communities as nodes and edges based on user overlap between the communities. however, the kind of clusteredness our method draws upon has been shown to be a property of various types of social networks, including social networks with individual users as nodes such as twitter @xcite . we expect slap4slip to be a suitable framework for finding polarized concepts in these cases, too. ## conclusion we introduce slap4slip (sparse language properties for social link prediction), a novel framework for finding linguistic features maximally informative about the structure of a social network, and show that it can be used to detect polarized concepts. we model polarization along the dimensions of salience and framing. while we only address polarized concepts in this paper, the general nature of the framework makes it possible to apply it in diverse scenarios involving linguistic data attached to social networks (e.g., to find the most pronounced topical differences in citation networks). we see our study as an exciting first step towards bringing together computational social science research on online polarization, nlp work on political language, and graph-based deep learning.