Othering is a social and discursive process through which an out-group is constructed as fundamentally different from and outside the boundaries of an in-group [5, 17, 22, 28]. For instance, a sentence such as “Our country is no place for people like them” frames the out-group as excluded from the community.

Othering is not simply the recognition of difference, but the construction of exclusionary distinctions between “us” and “them”. This is a process through which groups construct themselves as good, virtuous, and legitimate, while portraying others as inferior, threatening, dangerous, or morally wrong [5, 28]. In this sense, othering is closely tied to the perception of threat, the morality of the out-group, and the reinforcement of in-group belonging [5, 17].

Othering is often discussed in relation to race, ethnicity, religion, or migration. However, its manifestations are context-dependent and may become more complex in highly polarized political contexts. In such contexts, processes of othering may also be strongly structured by ideological and partisan affiliations, allowing political opponents to be framed not merely as adversaries, but as morally corrupt, dangerous, or incompatible with the values of the in-group. In this sense, the out-group is not restricted to traditional social categories. It may also include groups associated with particular political positions or ideological leanings. For example, the expression “Thieving president and those who still support that rat” targets not only a political leader but also his supporters, constructing an out-group defined by their support for that leader. Prior work has similarly shown that online othering can create moral boundaries between users, even when explicit group labels are absent or less clearly defined [28], as illustrated by statements such as “Those on the left or right have lost the right to an opinion, they only defend garbage”, in which political identities themselves can become the basis for exclusion and delegitimization [28]. In highly polarized environments, people who support a different political camp may be represented as enemies of the nation, threats to social order, or actors whose values are seen as incompatible with those of the in-group [1, 28].

In this study, political othering is defined as the discursive construction of a collective out-group as morally inferior, threatening, or not belonging to the in-group [28]. Unlike personal attacks or general criticism, othering requires a process of generalization that encompasses an entire social or political group. Political othering can overlap with hate speech but is not limited to it. Hate speech commonly refers to abusive or derogatory attacks [16, 30], whereas othering captures the broader discursive construction of an out-group as inferior and outside the boundaries of belonging, often without relying on explicit slurs or hateful expressions [28]. This distinction is particularly important in political discourse, where hostility may target partisan or geopolitical out-groups without constituting identity-based hate [15].

The study of online speech, particularly on social networks, has become a major research area in recent years. The anonymity provided by these platforms can contribute to the prevalence of toxic [6], abusive [29], discriminatory [39], extremist [29], and hateful [16] comments. The growing research interest is driven by the significant impact of online speech, particularly its role in attacks against minority groups, raising serious social and ethical concerns.

Flow diagram showing the two-step dataset construction pipeline, from expert annotation to LLM-assisted expansion and validation.

This harmful language tends to intensify during periods of social instability, such as economic crises, political polarization, or global health crises, in which externalized fears lead to the blame of specific groups [14]. In these contexts, the perception of an out-group threat becomes a primary psychological driver, justifying hostility as a defensive necessity. These dynamics are often reflected in both hate speech and fear speech. While hate speech is typically characterized by explicit dehumanization and the vilification of protected groups [26, 30], fear speech portrays an out-group as an existential threat to the in-group's survival or values [36]. Othering, however, is a broader discursive process through which groups are constructed as fundamentally different, illegitimate, inferior, or threatening. In this sense, hate and fear speech may be understood as possible manifestations of broader othering dynamics, although othering may also occur through subtler forms of political exclusion that do not contain explicit hate or threat language. These rhetorical strategies facilitate exclusion by reducing individuals to a single perceived group membership [21].

Beyond its general definition, othering language encompasses a broad sociological process of out-group construction that manifests through diverse social identities and contextual triggers [17]. Research indicates that the thematic focus of othering often shifts depending on the geopolitical landscape and the targeted group. For instance, studies analyzing conflict in Ukraine identified specific dimensions of othering, such as threats to cultural identity, perceived risks to physical survival, and explicit dehumanization through vilification [17]. Similarly, [36] observed that religious othering often centers on existential threats to faith communities. Furthermore, [1] proposed a framework for the detection of "enemy", measuring how othering targets race, religion, sexism, and disability, highlighting the nature of these biases in extremist rhetoric.

However, the targets of these exclusionary narratives also vary by social domain. Recent work by [28] highlights that political disaffiliation can also be a primary driver of othering. This is particularly salient in Latin America, where othering often manifests through intense ideological divisions. In these regions, the main enemy, the out-group, consists of neither foreigners nor immigrants, but the left, homosexuals, and feminists, adversaries that tend to merge into a single melting-pot [28] in which ideological opposition is framed as an existential threat to the nation's values.

Research Gap. While hate speech, and to some extent fear speech, have been extensively studied [6, 11, 36], othering remains comparatively understudied as a computational phenomenon. A central reason for this gap is the limited availability of datasets specifically designed to capture how exclusionary boundaries are constructed in discourse, particularly in political contexts. This gap is especially relevant to WhatsApp group communication. In Brazil, WhatsApp plays a central role in political debate, where public political groups often serve as highly polarized spaces for attacks on opponents [12, 24]. The platform offers a particularly suitable setting for studying how political opponents are discursively constructed as out-groups. We address this gap by introducing, to the best of our knowledge, the first Portuguese-language resource for studying political othering in Brazilian WhatsApp discourse. We aim to bridge the gap between sociological theory and computational linguistics, providing an important resource for analyzing the structure of ideological polarization and othering attacks.

This study builds on an existing source corpus of public political WhatsApp groups [23, 24]. The corpus was collected from public Brazilian political WhatsApp groups monitored between January 2022 and January 2023, a period covering the months before and after the 2022 presidential election in Brazil. This interval is particularly relevant due to intense political mobilization, misinformation surrounding the electoral process, and post-election protests. Political keywords were used to search for public WhatsApp invitation links shared on websites and social media platforms [23], and candidate groups were manually verified to ensure they were indeed political. In total, approximately 1,400 public political WhatsApp groups were monitored during this period.

In this work, we build on that corpus by identifying, annotating, and analyzing messages related to political othering. We focus specifically on text messages labeled by WhatsApp as “forwarded many times.” A key feature of WhatsApp is message forwarding, which allows users to share content across multiple chats and groups. The “forwarded many times” label is automatically displayed by the platform when a message exceeds a predefined forwarding threshold, indicating that it has circulated widely across group communities. We focus on these because, unlike organic messages, such content is often strategically crafted to maximize virality, disseminate political narratives at scale, mobilize activists, and coordinate partisan narratives across group communities [27]. By filtering the source corpus for messages marked as “forwarded many times”, we obtained 21,667 unique messages. This subset constitutes the dataset used in this study.

From this dataset, we first constructed a gold annotation set of 150 messages through manual annotation. This gold set was designed primarily as a calibration resource for the evaluated models. To establish a gold standard political othering dataset, we combined random sampling with keyword-based upsampling. We first generated an initial list of candidate keywords, including dehumanizing, demonizing, segregation, and threat-framing expressions, as well as election-related entities and themes identified through prior literature and domain knowledge (see Appendix A for the complete keyword list). These keywords were only used as an aid for sampling and to retrieve a pool of candidate messages from the corpus for manual labeling. Because othering is relatively sparse under random sampling, keyword-assisted sampling was used to ensure sufficient positive instances for reliable evaluation.

Using this sampled set, two trained annotators independently labeled 150 messages using the GATE Teamware 2 platform [45]. Before annotation, both annotators completed a training stage with curated examples of othering and non-othering, and were required to pass a brief qualification check to ensure their understanding of the guidelines. Annotation was conducted in five batches of 30 messages. For each batch, the annotators first labeled the messages independently. After each batch, disagreements were discussed to refine the guidelines, and final labels were subsequently assigned through consensus. Inter-annotator agreement, measured with Cohen's Kappa [9], was 0.62. This is consistent with the variability typically observed in subjective content annotation tasks, where agreement may range from moderate to substantial depending on label definition and task complexity [25, 43, 44]. We selected 150 messages to keep expert annotation feasible, allowing the annotators to carefully examine each message and iteratively refine the annotation guidelines. This decision also reflects the length and contextual complexity of the messages, which contain an average of 240 words and 14.55 sentences. The resulting gold annotation set contains 101 othering and 49 non-othering messages. Its class imbalance results from the keyword-assisted sampling strategy, which increased the number of positive instances. We therefore treat this gold set as a carefully calibrated reference that provides a reliable basis for evaluating and comparing model performance.

Even after filtering the corpus for unique messages via MD5 hash matching, substantial redundancy remains. Due to the nature of viral content, which is frequently forwarded across groups, the same core message often reappears with minor modifications, added emojis, spelling changes, or the inclusion/removal of short contextual phrases. As a result, exact deduplication alone is insufficient to eliminate repeated content, motivating an additional near-duplicate removal step based on semantic similarity.

To deal with this, we first computed sentence embeddings for all text messages and indexed them with FAISS [13], a library for efficient similarity search over dense vectors, to perform approximate nearest-neighbor retrieval at scale. For each message, we retrieved its k = 25 nearest neighbors and their cosine similarity scores. We then built a similarity graph by connecting pairs of messages with a cosine similarity ≥ 0.90. Within this graph, each connected component represents a set of messages linked by high-similarity relationships. Finally, we performed deduplication by retaining only a single representative message from each cluster.

Furthermore, to reduce noise unrelated to substantive political discourse, we applied additional text-quality filters to remove flooding and spam-like content, which are often characterized as malicious attacks intended to disrupt WhatsApp's conversational environment [24]. First, we discarded extremely long entries (> 5,000 characters), which are typically dominated by copy-pasted blocks or repeated sequences and are less suitable for manual and model-based annotation. Second, we removed very short entries (< 5 words), as these rarely contain enough linguistic context to reliably identify discursive framing. Finally, we filtered messages with a highly repetitive structure by removing messages in which more than 90% of the words were duplicates. After applying these filtering and deduplication criteria, we obtained a preprocessed corpus of 12,972 messages. This dataset provides the basis for our subsequent labeling phase and systematic analysis of othering.

To expand the gold-standard dataset and obtain labels at scale for the larger corpus, we adopted an LLM-assisted labeling strategy inspired by prior work on artificial annotators [18, 40]. In this approach, an automated classifier is calibrated against a small set of human-annotated examples and then used to propagate labels to a much larger collection of unlabeled messages. The goal is to approximate the decisions of trained human annotators while enabling scalable annotation. For this task, we use classifier models to propagate labels beyond the manually annotated subset, using the gold set as the reference for evaluation. To select a suitable model for this role, we benchmarked multiple candidate LLMs under a consistent experimental protocol, comparing them against traditional statistical classifiers, including Logistic Regression, Support Vector Machines (SVM), Random Forests, and XGBoost.

All candidate models were evaluated on the gold annotation set before being applied to the preprocessed corpus. The evaluation procedure differed according to the modeling strategy. The supervised baselines were evaluated using repeated stratified 5-fold cross-validation, whereas the LLMs were evaluated through repeated inference over the gold set using a fixed prompt configuration. To reduce stochastic variation, we fixed the random seed (42) across experiments. Class imbalance was handled by using class-weighted training for the supervised baselines, assigning a higher weight to the minority class during optimization.

Strategy for Classification Models. For the classical baselines, texts were represented using TF–IDF features based on unigrams and bigrams. Following the same text preprocessing pipeline, we evaluated Logistic Regression (liblinear, $max\iter=2000$), Linear SVM (C = 1), Random Forest (n = 1000, $min\samples\split$ =5), and XGBoost (n = 800, η = 0.05). We also evaluated a transformer baseline for Brazilian Portuguese (BERTimbau) [38], using the base pre-trained model. We fine-tuned BERTimbau on the training partition of each fold in a stratified 5-fold cross-validation setup. Fine-tuning was performed for five epochs with mixed precision (FP16), per-device batch size of 8, and gradient accumulation over two steps. We report its performance alongside the classical baselines and the LLM-based classifiers.

Strategy for LLMs. These were evaluated under an in-context learning (ICL) setup, without specific fine-tuning or gradient updates. We employed a single, fixed prompt template and varied only the number of in-context examples, with k ∈ {2, 4, 8} to perform 2-shot, 4-shot, and 8-shot prompting. For each setup, the k examples were balanced, being evenly divided between the two target classes. Preliminary comparisons across these settings indicated modest and unstable differences in Macro-F1 on the gold dataset. We therefore adopt the k = 2 configuration as a fixed reference condition for the comparative evaluation. Prompts were executed with low-temperature sampling (T = 0.2) to balance output consistency with some flexibility in generation. Each message was evaluated independently using the same prompt template, with no conversational history. In the few-shot setting, labeled examples preceded the target message and served as task demonstrations. All experiments were conducted using a Portuguese prompt (see Appendix B for the full template).

Unlike setups that restrict outputs to a single binary label, our prompt required a structured output containing: (i) a binary decision for othering (is_othering $in lbrace texttt{yes},texttt{no}rbrace$); (ii) a confidence score; and (iii) auxiliary fields including a brief justification and supporting evidence span, for auditing and error analysis. For quantitative evaluation, we used only the label ($texttt{is\othering}$), with the remaining fields treated as metadata. Model outputs were parsed into our structured schema, and we additionally employed a high-confidence filtering strategy for dataset expansion.

Table 2: Manual audit of high-confidence labels produced after applying each model to the full dataset. The audit uses a manually labeled sample of 150 messages. κ denotes Cohen's kappa between model predictions and audit labels. † denotes high-confidence filtering (conf ≥ 0.90) and N denotes the number of messages retained after applying the filter.

Table 1 reports performance of the tested models using Macro-F1 and Cohen's Kappa (κ) as our primary metrics, complemented by class-specific precision, recall, and F1-score. Given the unequal distribution in the gold annotation set, we emphasize Macro-F1, which captures balanced performance across the two classes, whereas κ measures agreement between the model predictions and the human gold labels. Intuitively, κ can be interpreted as the extent to which the model behaves like an additional annotator. While results vary across all models, the main differences arise between LLM-based approaches and classical baselines. For LLM-based classifiers, we repeated inference six times for each model and report the mean and 95% confidence interval across runs. For the statistical baselines, we used repeated stratified 5-fold cross-validation with five random seeds. For each seed, predictions from the five folds were concatenated before computing the evaluation metrics. We then report the mean and 95% confidence interval across the five repetitions.

As shown in Table 1, among the large language models, Llama-3.1-8B achieves the strongest overall performance (κ = 0.654 ± 0.012, Macro-F1 = 0.826 ± 0.006), outperforming Gemma-2-9B, Qwen-2.5-7B, and Ministral-8B. By contrast, classical statistical models show substantially lower agreement (κ ≈ 0.267 for Random Forest) and lower Macro-F1 (≈ 0.633), suggesting that TF–IDF n-gram features have limited capacity to capture the contextual dependencies involved in othering. The classical baselines tend to over-predict the othering class, resulting in a substantial number of false positives (see Appendix C for details). Although the LLMs capture relevant patterns of othering, their unfiltered predictions still exhibit non-negligible disagreement with the gold labels. Applying these predictions directly to a substantially larger corpus could propagate classification errors during dataset expansion.

To mitigate this limitation, we investigate whether the confidence value generated by each model can be used as an abstention criterion. For each message, the models were instructed to return a binary label together with a confidence score between 0 and 1. This score was generated directly by the model as part of the structured output and was not derived from token probabilities. We interpret this value as the model's self-reported certainty rather than as a calibrated probability. As models may differ in how they report confidence, we interpret the resulting scores within each model rather than comparing them directly across models.

The limited resolution of continuous self-reported confidence is particularly evident for Qwen-2.5-7B and Ministral-8B, whose confidence values for predictions labeled as othering are concentrated at or above 0.90. Consequently, the threshold provides little discrimination between more and less reliable positive predictions for these models. By contrast, the scores generated by Gemma-2-9B and Llama-3.1-8B show a more informative relationship with prediction quality, with higher-confidence subsets exhibiting greater agreement with the human annotations.

We therefore use self-reported confidence as a filtering criterion evaluated separately for each model. Given a confidence threshold, predictions below the threshold are excluded rather than reassigned to the non-othering class. We evaluated thresholds of 0.80, 0.85, and 0.90 on the manually annotated data, examining the trade-off between agreement and coverage for each model. We adopted 0.90 because it produced the highest agreement among the evaluated thresholds while retaining sufficient coverage for Gemma and Llama. In Table 1, rows marked with † report performance only on predictions retained at the selected threshold.

As shown in Table 1, when evaluated on the predictions retained at this threshold, Gemma-2-9B achieves κ = 0.726 and a Macro-F1 of 0.863, compared with κ = 0.550 and a Macro-F1 of 0.771 when all predictions are considered. Similarly, Llama-3.1-8B achieves κ = 0.707 and a Macro-F1 of 0.852, compared with κ = 0.654 and a Macro-F1 of 0.826 on the full set of predictions. These results indicate that confidence-based filtering provides a useful trade-off between label quality and coverage for both Gemma and Llama.

Table 3: Representative examples of model misclassifications identified in the qualitative error analysis. FP and FN indicate false-positive and false-negative predictions, respectively. Examples are shortened for readability.

Based on the confidence-filtered benchmark, we retained Gemma-2-9B† and Llama-3.1-8B† as the two strongest candidate artificial annotators. We applied both models to the full corpus using the same confidence threshold (≥ 0.90) and manually audited a sample of their predictions, as shown in Table 2. Applying the confidence criterion to the preprocessed corpus of 12,972 messages, Gemma-2-9B† retained 8,592 predictions (66.2% coverage), whereas Llama-3.1-8B† retained 9,663 predictions (74.5% coverage).

To evaluate the quality of the retained labels beyond the original gold set, we manually audited an additional random sample of 150 predictions from each model. This audit provided an independent assessment of the reliability of the expanded dataset and enabled a careful inspection of the models’ errors and overall behavior. As shown in Table 2, Gemma-2-9B† achieved a Macro-F1 of 0.900, and κ = 0.801. Llama-3.1-8B† achieved a Macro-F1 of 0.834, and κ = 0.675. Together with the gold-set results, the audit indicates that confidence filtering produces a reliable subset of silver labels, with Gemma showing stronger agreement with human judgments. The audit also reveals model-specific differences: Gemma achieves a precision of 1.00 for non-othering and 0.719 for othering, whereas Llama achieves a lower othering precision of 0.636.

Differences between the gold-set evaluation and the manual audit may partly reflect their distinct sampling strategies. The gold dataset was constructed via keyword-based upsampling to ensure sufficient positive instances, whereas the audit sample was randomly drawn from the high-confidence predictions retained from the full corpus. These different procedures may produce samples with different class distributions and levels of ambiguity. Our error inspection further suggests that Llama tends to over-predict othering in negative messages that target individuals or institutions but do not generalize to an out-group. An example of this pattern is shown in Table 3, where the model misclassifies a case of personal vilification as othering even though the message does not construct a group-based exclusionary boundary. In other words, the model appears to rely more on aggressive tone and toxic language and has greater difficulty identifying the generalization step that distinguishes political othering from individual criticism, which leads to false positives in the large-scale labeling.

Based on the manual audit, we selected the high-confidence predictions from Gemma-2-9B† as the final labels used in the subsequent analyses. Although Llama retains a larger proportion of the corpus, Gemma achieves substantially stronger agreement with human judgments and fewer false positives. The confidence threshold retains approximately two-thirds of the corpus, providing a favorable balance between label quality and coverage.

The final labeled dataset contains 8,592 messages, including 7,203 non-othering and 1,389 othering messages. A limitation of this strategy is that high-confidence filtering may exclude more ambiguous or difficult examples, reducing the representation of borderline cases. Nevertheless, we consider this an acceptable trade-off, since our primary goal is to obtain labels with higher reliability. The anonymized dataset is available on Zenodo.1

Table 4: Distribution of consolidated target categories in othering messages. Frequencies indicate the number of othering messages in each category. Messages may include targets from multiple categories. The final column presents target expressions.

In this section, we analyze the most frequent targets of political othering, focusing on the othering subset of the final labeled dataset, which comprises 1,389 messages. We use the target labels generated by the model as part of its structured output. We employ these labels as an interpretive layer to understand which types of groups the model associates with political othering when assigning positive labels. This allows us to examine both the frequency of different targets and the main groups that become the focus of othering attacks. Because these labels often contained multiple targets and different formulations referring to the same group, we manually reviewed all othering messages and assigned one or more broader target categories. During this process, semantically equivalent expressions, such as references to leftists and left-wing voters, were consolidated into the same category. We identified 12 distinct target categories, as detailed in Table 4. For each target category, the frequency corresponds to the number of othering messages associated with that category. However, a single message may include multiple target categories and, therefore, contribute to more than one frequency count. Consequently, the sum of the category frequencies may exceed the total of 1,389 othering messages.

Table 5: Top 10 LIWC categories for othering and non-othering messages, ranked by Pearson correlation (|r|) between normalized frequencies and binary labels. All correlations are significant at p < 0.01.

Most instances of political othering are directed at explicitly political targets, especially Left-wing Supporters, Right-wing Supporters, and Generic Political Opponents. This suggests that othering is used primarily as a strategy to delegitimize political adversaries by portraying them as morally inferior, threatening, or fundamentally opposed to the in-group. For example, one message states that “leftists are bums, the kind of bums who depend on government assistance”, explicitly framing political opponents as lazy, dependent, and inferior. In this sense, political othering appears closely tied to partisan conflict and ideological polarization, functioning as a discursive mechanism that transforms political opponents into a hostile and stigmatized group.

At the same time, the presence of categories such as Gender and Sexuality Groups, Racial/Ethnic Groups, and Social Movements shows that othering is not restricted to electoral competition alone. Attacks against minorities, immigrants, Black and Indigenous people reflect a more classical form of exclusion, in which vulnerable or historically marginalized groups are framed as threats or as incompatible with the in-group. In this sense, political othering in our data also intersects with broader patterns of social exclusion.

This broader distribution of targets also reveals that they are not always framed as explicit social groups. Often, the target is formulated through the name of an institution, such as a federal court, legislative body, or media organization, and serves as a proxy for the broader social or collective political enemy associated with it. In these cases, institutional criticism may function as a proxy for stigmatizing an external group. While this highlights the analytical importance of identifying political othering, it also introduces subjectivity, since the boundary between legitimate criticism and out-group construction is not always straightforward.

This pattern is particularly evident in attacks against journalists and the media. References to outlets such as Globo TV and Band TV illustrate how institutional actors are repositioned as political antagonists. For example, one message states that “The media is trying to create leaks on a ship that we are all on board”, portraying the press as an internal enemy acting deliberately against the collective good. By framing the press as a corrupt or biased entity aligned with “the enemy”, these messages transform media criticism into a tool for group-based othering. Such discourse also creates a favorable environment for conspiracy narratives and disinformation.

A similar dynamic appears in the category State Institutions and Authorities, highlighting a particularly relevant feature of the Brazilian context. This is especially significant in Brazil, where the Supreme Court and electoral authorities have become prominent targets in narratives concerning electoral fraud and political persecution. For example, one message states that “The Supreme Court justices are actually communists and leftists who want the destruction of Brazil”, portraying state institutions and their representatives as political enemies through their perceived association with leftist ideologies. In these messages, othering is used to discredit judicial actors by framing them not as neutral institutions, but as enemies aligned with a political side. In this polarized narrative, not only were the justices vilified, but their defenders were also labeled as complicit enemies of the people, a framing also reflected in repeated protests against the Supreme Court in Brazil [8].

These findings highlight the broad scope of political othering. It is mobilized against a wide range of actors, including political opponents, marginalized groups, social movements, journalists, state institutions, and security forces. This diversity suggests that political othering is not merely an expression of partisan hostility, but a flexible discursive strategy that adapts to the dynamics of political conflict. In this sense, the variety of targets reflects specific features of the political environment where different actors can be constructed as threatening and incompatible with the in-group.

In this analysis, we characterize each message using the psycholinguistic categories of the Portuguese LIWC [7], comparing the othering subset with the non-othering messages of the final labeled dataset. For each message, we calculate the proportion of words assigned to each LIWC category and measure its Pearson correlation with the binary othering label. Table 5 presents the ten categories most strongly associated with othering and non-othering messages.

The most distinctive pattern among othering messages is the prominence of negative affect and interpersonal reference. The categories negemo and anger indicate that these messages more frequently employ vocabulary associated with negative emotional evaluation and hostility. Although negative language is expected in antagonistic communication, its association with pronoun categories such as they and you suggests that negative evaluations are often directed toward specific targets. Othering messages therefore appear to be organized around a relational structure in which a speaker confronts or attributes negative characteristics to the out-group. Othering messages are also associated with the categories conj, function, and excl. This indicates that differences between the classes extend beyond explicitly emotional terms and also involve the sentence's functional organization. These exclusion terms may contribute to constructions that distinguish one group from another. The prominence of exclusion-related terms, such as but and except, may point to the role of boundary-making in othering discourse. Such constructions may linguistically reinforce distinctions by defining who belongs to the in-group and who is positioned outside it. From this perspective, the in-group is constructed not only through shared characteristics, but also through its opposition to and exclusion of an out-group.

In contrast, non-othering messages are more strongly associated with categories such as relativ, time, past, and motion. These categories point toward a greater emphasis on temporal relations, events, and actions. The presence of hear and insight further suggests that non-othering messages more frequently report information and describe experiences. This does not imply that such messages are neutral. This means that their linguistic focus seems to be less centered on the negative characterization of social actors.

The contribution of this analysis is to show that othering is characterized not simply by negative emotion, but by the combination of negative evaluation and explicit references to the out-group. These findings reveal consistent aggregate differences between othering and non-othering messages. Although individual LIWC categories should not be interpreted as isolated deterministic markers of othering, their combined psycholinguistic pattern highlights that this discourse relies on a systematic intersection of negative evaluation and the structural marking of boundaries.