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Introduction
“Ché saetta previsa vien più lenta.”
– Dante Alighieri, Divina Commedia, Paradiso
Antisocial behavior is a persistent problem plaguing online conversation platforms; it is both widespread BIBREF0 and potentially damaging to mental and emotional health BIBREF1, BIBREF2. The strain this phenomenon puts on community maintainers has sparked recent interest in computational approaches for assisting human moderators.
Prior work in this direction has largely focused on post-hoc identification of various kinds of antisocial behavior, including hate speech BIBREF3, BIBREF4, harassment BIBREF5, personal attacks BIBREF6, and general toxicity BIBREF7. The fact that these approaches only identify antisocial content after the fact limits their practicality as tools for assisting pre-emptive moderation in conversational domains.
Addressing this limitation requires forecasting the future derailment of a conversation based on early warning signs, giving the moderators time to potentially intervene before any harm is done (BIBREF8 BIBREF8, BIBREF9 BIBREF9, see BIBREF10 BIBREF10 for a discussion). Such a goal recognizes derailment as emerging from the development of the conversation, and belongs to the broader area of conversational forecasting, which includes future-prediction tasks such as predicting the eventual length of a conversation BIBREF11, whether a persuasion attempt will eventually succeed BIBREF12, BIBREF13, BIBREF14, whether team discussions will eventually lead to an increase in performance BIBREF15, or whether ongoing counseling conversations will eventually be perceived as helpful BIBREF16.
Approaching such conversational forecasting problems, however, requires overcoming several inherent modeling challenges. First, conversations are dynamic and their outcome might depend on how subsequent comments interact with each other. Consider the example in Figure FIGREF2: while no individual comment is outright offensive, a human reader can sense a tension emerging from their succession (e.g., dismissive answers to repeated questioning). Thus a forecasting model needs to capture not only the content of each individual comment, but also the relations between comments. Previous work has largely relied on hand-crafted features to capture such relations—e.g., similarity between comments BIBREF16, BIBREF12 or conversation structure BIBREF17, BIBREF18—, though neural attention architectures have also recently shown promise BIBREF19.
The second modeling challenge stems from the fact that conversations have an unknown horizon: they can be of varying lengths, and the to-be-forecasted event can occur at any time. So when is it a good time to make a forecast? Prior work has largely proposed two solutions, both resulting in important practical limitations. One solution is to assume (unrealistic) prior knowledge of when the to-be-forecasted event takes place and extract features up to that point BIBREF20, BIBREF8. Another compromising solution is to extract features from a fixed-length window, often at the start of the conversation BIBREF21, BIBREF15, BIBREF16, BIBREF9. Choosing a catch-all window-size is however impractical: short windows will miss information in comments they do not encompass (e.g., a window of only two comments would miss the chain of repeated questioning in comments 3 through 6 of Figure FIGREF2), while longer windows risk missing the to-be-forecasted event altogether if it occurs before the end of the window, which would prevent early detection.
In this work we introduce a model for forecasting conversational events that overcomes both these inherent challenges by processing comments, and their relations, as they happen (i.e., in an online fashion). Our main insight is that models with these properties already exist, albeit geared toward generation rather than prediction: recent work in context-aware dialog generation (or “chatbots”) has proposed sequential neural models that make effective use of the intra-conversational dynamics BIBREF22, BIBREF23, BIBREF24, while concomitantly being able to process the conversation as it develops (see BIBREF25 for a survey).
In order for these systems to perform well in the generative domain they need to be trained on massive amounts of (unlabeled) conversational data. The main difficulty in directly adapting these models to the supervised domain of conversational forecasting is the relative scarcity of labeled data: for most forecasting tasks, at most a few thousands labeled examples are available, insufficient for the notoriously data-hungry sequential neural models.
To overcome this difficulty, we propose to decouple the objective of learning a neural representation of conversational dynamics from the objective of predicting future events. The former can be pre-trained on large amounts of unsupervised data, similarly to how chatbots are trained. The latter can piggy-back on the resulting representation after fine-tuning it for classification using relatively small labeled data. While similar pre-train-then-fine-tune approaches have recently achieved state-of-the-art performance in a number of NLP tasks—including natural language inference, question answering, and commonsense reasoning (discussed in Section SECREF2)—to the best of our knowledge this is the first attempt at applying this paradigm to conversational forecasting.
To test the effectiveness of this new architecture in forecasting derailment of online conversations, we develop and distribute two new datasets. The first triples in size the highly curated `Conversations Gone Awry' dataset BIBREF9, where civil-starting Wikipedia Talk Page conversations are crowd-labeled according to whether they eventually lead to personal attacks; the second relies on in-the-wild moderation of the popular subreddit ChangeMyView, where the aim is to forecast whether a discussion will later be subject to moderator action due to “rude or hostile” behavior. In both datasets, our model outperforms existing fixed-window approaches, as well as simpler sequential baselines that cannot account for inter-comment relations. Furthermore, by virtue of its online processing of the conversation, our system can provide substantial prior notice of upcoming derailment, triggering on average 3 comments (or 3 hours) before an overtly toxic comment is posted.
To summarize, in this work we:
introduce the first model for forecasting conversational events that can capture the dynamics of a conversation as it develops;
build two diverse datasets (one entirely new, one extending prior work) for the task of forecasting derailment of online conversations;
compare the performance of our model against the current state-of-the-art, and evaluate its ability to provide early warning signs.
Our work is motivated by the goal of assisting human moderators of online communities by preemptively signaling at-risk conversations that might deserve their attention. However, we caution that any automated systems might encode or even amplify the biases existing in the training data BIBREF26, BIBREF27, BIBREF28, so a public-facing implementation would need to be exhaustively scrutinized for such biases BIBREF29.
Further Related Work
Antisocial behavior. Antisocial behavior online comes in many forms, including harassment BIBREF30, cyberbullying BIBREF31, and general aggression BIBREF32. Prior work has sought to understand different aspects of such behavior, including its effect on the communities where it happens BIBREF33, BIBREF34, the actors involved BIBREF35, BIBREF36, BIBREF37, BIBREF38 and connections to the outside world BIBREF39.
Post-hoc classification of conversations. There is a rich body of prior work on classifying the outcome of a conversation after it has concluded, or classifying conversational events after they happened. Many examples exist, but some more closely related to our present work include identifying the winner of a debate BIBREF40, BIBREF41, BIBREF42, identifying successful negotiations BIBREF21, BIBREF43, as well as detecting whether deception BIBREF44, BIBREF45, BIBREF46 or disagreement BIBREF47, BIBREF48, BIBREF49, BIBREF50, BIBREF51 has occurred.
Our goal is different because we wish to forecast conversational events before they happen and while the conversation is still ongoing (potentially allowing for interventions). Note that some post-hoc tasks can also be re-framed as forecasting tasks (assuming the existence of necessary labels); for instance, predicting whether an ongoing conversation will eventually spark disagreement BIBREF18, rather than detecting already-existing disagreement.
Conversational forecasting. As described in Section SECREF1, prior work on forecasting conversational outcomes and events has largely relied on hand-crafted features to capture aspects of conversational dynamics. Example feature sets include statistical measures based on similarity between utterances BIBREF16, sentiment imbalance BIBREF20, flow of ideas BIBREF20, increase in hostility BIBREF8, reply rate BIBREF11 and graph representations of conversations BIBREF52, BIBREF17. By contrast, we aim to automatically learn neural representations of conversational dynamics through pre-training.
Such hand-crafted features are typically extracted from fixed-length windows of the conversation, leaving unaddressed the problem of unknown horizon. While some work has trained multiple models for different window-lengths BIBREF8, BIBREF18, they consider these models to be independent and, as such, do not address the issue of aggregating them into a single forecast (i.e., deciding at what point to make a prediction). We implement a simple sliding windows solution as a baseline (Section SECREF5).
Pre-training for NLP. The use of pre-training for natural language tasks has been growing in popularity after recent breakthroughs demonstrating improved performance on a wide array of benchmark tasks BIBREF53, BIBREF54. Existing work has generally used a language modeling objective as the pre-training objective; examples include next-word prediction BIBREF55, sentence autoencoding, BIBREF56, and machine translation BIBREF57. BERT BIBREF58 introduces a variation on this in which the goal is to predict the next sentence in a document given the current sentence. Our pre-training objective is similar in spirit, but operates at a conversation level, rather than a document level. We hence view our objective as conversational modeling rather than (only) language modeling. Furthermore, while BERT's sentence prediction objective is framed as a multiple-choice task, our objective is framed as a generative task.
Derailment Datasets
We consider two datasets, representing related but slightly different forecasting tasks. The first dataset is an expanded version of the annotated Wikipedia conversations dataset from BIBREF9. This dataset uses carefully-controlled crowdsourced labels, strictly filtered to ensure the conversations are civil up to the moment of a personal attack. This is a useful property for the purposes of model analysis, and hence we focus on this as our primary dataset. However, we are conscious of the possibility that these strict labels may not fully capture the kind of behavior that moderators care about in practice. We therefore introduce a secondary dataset, constructed from the subreddit ChangeMyView (CMV) that does not use post-hoc annotations. Instead, the prediction task is to forecast whether the conversation will be subject to moderator action in the future.
Wikipedia data. BIBREF9's `Conversations Gone Awry' dataset consists of 1,270 conversations that took place between Wikipedia editors on publicly accessible talk pages. The conversations are sourced from the WikiConv dataset BIBREF59 and labeled by crowdworkers as either containing a personal attack from within (i.e., hostile behavior by one user in the conversation directed towards another) or remaining civil throughout.
A series of controls are implemented to prevent models from picking up on trivial correlations. To prevent models from capturing topic-specific information (e.g., political conversations are more likely to derail), each attack-containing conversation is paired with a clean conversation from the same talk page, where the talk page serves as a proxy for topic. To force models to actually capture conversational dynamics rather than detecting already-existing toxicity, human annotations are used to ensure that all comments preceding a personal attack are civil.
To the ends of more effective model training, we elected to expand the `Conversations Gone Awry' dataset, using the original annotation procedure. Since we found that the original data skewed towards shorter conversations, we focused this crowdsourcing run on longer conversations: ones with 4 or more comments preceding the attack. Through this additional crowdsourcing, we expand the dataset to 4,188 conversations, which we are publicly releasing as part of the Cornell Conversational Analysis Toolkit (ConvoKit).
We perform an 80-20-20 train/dev/test split, ensuring that paired conversations end up in the same split in order to preserve the topic control. Finally, we randomly sample another 1 million conversations from WikiConv to use for the unsupervised pre-training of the generative component.
Reddit CMV data. The CMV dataset is constructed from conversations collected via the Reddit API. In contrast to the Wikipedia-based dataset, we explicitly avoid the use of post-hoc annotation. Instead, we use as our label whether a conversation eventually had a comment removed by a moderator for violation of Rule 2: “Don't be rude or hostile to other users”.
Though the lack of post-hoc annotation limits the degree to which we can impose controls on the data (e.g., some conversations may contain toxic comments not flagged by the moderators) we do reproduce as many of the Wikipedia data's controls as we can. Namely, we replicate the topic control pairing by choosing pairs of positive and negative examples that belong to the same top-level post, following BIBREF12; and enforce that the removed comment was made by a user who was previously involved in the conversation. This process results in 6,842 conversations, to which we again apply a pair-preserving 80-20-20 split. Finally, we gather over 600,000 conversations that do not include any removed comment, for unsupervised pre-training.
Conversational Forecasting Model
We now describe our general model for forecasting future conversational events. Our model integrates two components: (a) a generative dialog model that learns to represent conversational dynamics in an unsupervised fashion; and (b) a supervised component that fine-tunes this representation to forecast future events. Figure FIGREF13 provides an overview of the proposed architecture, henceforth CRAFT (Conversational Recurrent Architecture for ForecasTing).
Terminology. For modeling purposes, we treat a conversation as a sequence of $N$ comments $C = \lbrace c_1,\dots ,c_N\rbrace $. Each comment, in turn, is a sequence of tokens, where the number of tokens may vary from comment to comment. For the $n$-th comment ($1 \le n \le N)$, we let $M_n$ denote the number of tokens. Then, a comment $c_n$ can be represented as a sequence of $M_n$ tokens: $c_n = \lbrace w_1,\dots ,w_{M_n}\rbrace $.
Generative component. For the generative component of our model, we use a hierarchical recurrent encoder-decoder (HRED) architecture BIBREF60, a modified version of the popular sequence-to-sequence (seq2seq) architecture BIBREF61 designed to account for dependencies between consecutive inputs. BIBREF23 showed that HRED can successfully model conversational context by encoding the temporal structure of previously seen comments, making it an ideal fit for our use case. Here, we provide a high-level summary of the HRED architecture, deferring deeper technical discussion to BIBREF60 and BIBREF23.
An HRED dialog model consists of three components: an utterance encoder, a context encoder, and a decoder. The utterance encoder is responsible for generating semantic vector representations of comments. It consists of a recurrent neural network (RNN) that reads a comment token-by-token, and on each token $w_m$ updates a hidden state $h^{\text{enc}}$ based on the current token and the previous hidden state:
where $f^{\text{RNN}}$ is a nonlinear gating function (our implementation uses GRU BIBREF62). The final hidden state $h^{\text{enc}}_M$ can be viewed as a vector encoding of the entire comment.
Running the encoder on each comment $c_n$ results in a sequence of $N$ vector encodings. A second encoder, the context encoder, is then run over this sequence:
Each hidden state $h^{\text{con}}_n$ can then be viewed as an encoding of the full conversational context up to and including the $n$-th comment. To generate a response to comment $n$, the context encoding $h^{\text{con}}_n$ is used to initialize the hidden state $h^{\text{dec}}_{0}$ of a decoder RNN. The decoder produces a response token by token using the following recurrence:
where $f^{\text{out}}$ is some function that outputs a probability distribution over words; we implement this using a simple feedforward layer. In our implementation, we further augment the decoder with attention BIBREF63, BIBREF64 over context encoder states to help capture long-term inter-comment dependencies. This generative component can be pre-trained using unlabeled conversational data.
Prediction component. Given a pre-trained HRED dialog model, we aim to extend the model to predict from the conversational context whether the to-be-forecasted event will occur. Our predictor consists of a multilayer perceptron (MLP) with 3 fully-connected layers, leaky ReLU activations between layers, and sigmoid activation for output. For each comment $c_n$, the predictor takes as input the context encoding $h^{\text{con}}_n$ and forwards it through the MLP layers, resulting in an output score that is interpreted as a probability $p_{\text{event}}(c_{n+1})$ that the to-be-forecasted event will happen (e.g., that the conversation will derail).
Training the predictive component starts by initializing the weights of the encoders to the values learned in pre-training. The main training loop then works as follows: for each positive sample—i.e., a conversation containing an instance of the to-be-forecasted event (e.g., derailment) at comment $c_e$—we feed the context $c_1,\dots ,c_{e-1}$ through the encoder and classifier, and compute cross-entropy loss between the classifier output and expected output of 1. Similarly, for each negative sample—i.e., a conversation where none of the comments exhibit the to-be-forecasted event and that ends with $c_N$—we feed the context $c_1,\dots ,c_{N-1}$ through the model and compute loss against an expected output of 0.
Note that the parameters of the generative component are not held fixed during this process; instead, backpropagation is allowed to go all the way through the encoder layers. This process, known as fine-tuning, reshapes the representation learned during pre-training to be more directly useful to prediction BIBREF55.
We implement the model and training code using PyTorch, and we are publicly releasing our implementation and the trained models together with the data as part of ConvoKit.
Forecasting Derailment
We evaluate the performance of CRAFT in the task of forecasting conversational derailment in both the Wikipedia and CMV scenarios. To this end, for each of these datasets we pre-train the generative component on the unlabeled portion of the data and fine-tune it on the labeled training split (data size detailed in Section SECREF3).
In order to evaluate our sequential system against conversational-level ground truth, we need to aggregate comment level predictions. If any comment in the conversation triggers a positive prediction—i.e., $p_{\text{event}}(c_{n+1})$ is greater than a threshold learned on the development split—then the respective conversation is predicted to derail. If this forecast is triggered in a conversation that actually derails, but before the derailment actually happens, then the conversation is counted as a true positive; otherwise it is a false positive. If no positive predictions are triggered for a conversation, but it actually derails then it counts as a false negative; if it does not derail then it is a true negative.
Fixed-length window baselines. We first seek to compare CRAFT to existing, fixed-length window approaches to forecasting. To this end, we implement two such baselines: Awry, which is the state-of-the-art method proposed in BIBREF9 based on pragmatic features in the first comment-reply pair, and BoW, a simple bag-of-words baseline that makes a prediction using TF-IDF weighted bag-of-words features extracted from the first comment-reply pair.
Online forecasting baselines. Next, we consider simpler approaches for making forecasts as the conversations happen (i.e., in an online fashion). First, we propose Cumulative BoW, a model that recomputes bag-of-words features on all comments seen thus far every time a new comment arrives. While this approach does exhibit the desired behavior of producing updated predictions for each new comment, it fails to account for relationships between comments.
This simple cumulative approach cannot be directly extended to models whose features are strictly based on a fixed number of comments, like Awry. An alternative is to use a sliding window: for a feature set based on a window of $W$ comments, upon each new comment we can extract features from a window containing that comment and the $W-1$ comments preceding it. We apply this to the Awry method and call this model Sliding Awry. For both these baselines, we aggregate comment-level predictions in the same way as in our main model.
CRAFT ablations. Finally, we consider two modified versions of the CRAFT model in order to evaluate the impact of two of its key components: (1) the pre-training step, and (2) its ability to capture inter-comment dependencies through its hierarchical memory.
To evaluate the impact of pre-training, we train the prediction component of CRAFT on only the labeled training data, without first pre-training the encoder layers with the unlabeled data. We find that given the relatively small size of labeled data, this baseline fails to successfully learn, and ends up performing at the level of random guessing. This result underscores the need for the pre-training step that can make use of unlabeled data.
To evaluate the impact of the hierarchical memory, we implement a simplified version of CRAFT where the memory size of the context encoder is zero (CRAFT $-$ CE), thus effectively acting as if the pre-training component is a vanilla seq2seq model. In other words, this model cannot capture inter-comment dependencies, and instead at each step makes a prediction based only on the utterance encoding of the latest comment.
Results. Table TABREF17 compares CRAFT to the baselines on the test splits (random baseline is 50%) and illustrates several key findings. First, we find that unsurprisingly, accounting for full conversational context is indeed helpful, with even the simple online baselines outperforming the fixed-window baselines. On both datasets, CRAFT outperforms all baselines (including the other online models) in terms of accuracy and F1. Furthermore, although it loses on precision (to CRAFT $-$ CE) and recall (to Cumulative BoW) individually on the Wikipedia data, CRAFT has the superior balance between the two, having both a visibly higher precision-recall curve and larger area under the curve (AUPR) than the baselines (Figure FIGREF20). This latter property is particularly useful in a practical setting, as it allows moderators to tune model performance to some desired precision without having to sacrifice as much in the way of recall (or vice versa) compared to the baselines and pre-existing solutions.
Analysis
We now examine the behavior of CRAFT in greater detail, to better understand its benefits and limitations. We specifically address the following questions: (1) How much early warning does the the model provide? (2) Does the model actually learn an order-sensitive representation of conversational context?
Early warning, but how early? The recent interest in forecasting antisocial behavior has been driven by a desire to provide pre-emptive, actionable warning to moderators. But does our model trigger early enough for any such practical goals?
For each personal attack correctly forecasted by our model, we count the number of comments elapsed between the time the model is first triggered and the attack. Figure FIGREF22 shows the distribution of these counts: on average, the model warns of an attack 3 comments before it actually happens (4 comments for CMV). To further evaluate how much time this early warning would give to the moderator, we also consider the difference in timestamps between the comment where the model first triggers and the comment containing the actual attack. Over 50% of conversations get at least 3 hours of advance warning (2 hours for CMV). Moreover, 39% of conversations get at least 12 hours of early warning before they derail.
Does order matter? One motivation behind the design of our model was the intuition that comments in a conversation are not independent events; rather, the order in which they appear matters (e.g., a blunt comment followed by a polite one feels intuitively different from a polite comment followed by a blunt one). By design, CRAFT has the capacity to learn an order-sensitive representation of conversational context, but how can we know that this capacity is actually used? It is conceivable that the model is simply computing an order-insensitive “bag-of-features”. Neural network models are notorious for their lack of transparency, precluding an analysis of how exactly CRAFT models conversational context. Nevertheless, through two simple exploratory experiments, we seek to show that it does not completely ignore comment order.
The first experiment for testing whether the model accounts for comment order is a prefix-shuffling experiment, visualized in Figure FIGREF23. For each conversation that the model predicts will derail, let $t$ denote the index of the triggering comment, i.e., the index where the model first made a derailment forecast. We then construct synthetic conversations by taking the first $t-1$ comments (henceforth referred to as the prefix) and randomizing their order. Finally, we count how often the model no longer predicts derailment at index $t$ in the synthetic conversations. If the model were ignoring comment order, its prediction should remain unchanged (as it remains for the Cumulative BoW baseline), since the actual content of the first $t$ comments has not changed (and CRAFT inference is deterministic). We instead find that in roughly one fifth of cases (12% for CMV) the model changes its prediction on the synthetic conversations. This suggests that CRAFT learns an order-sensitive representation of context, not a mere “bag-of-features”.
To more concretely quantify how much this order-sensitive context modeling helps with prediction, we can actively prevent the model from learning and exploiting any order-related dynamics. We achieve this through another type of shuffling experiment, where we go back even further and shuffle the comment order in the conversations used for pre-training, fine-tuning and testing. This procedure preserves the model's ability to capture signals present within the individual comments processed so far, as the utterance encoder is unaffected, but inhibits it from capturing any meaningful order-sensitive dynamics. We find that this hurts the model's performance (65% accuracy for Wikipedia, 59.5% for CMV), lowering it to a level similar to that of the version where we completely disable the context encoder.
Taken together, these experiments provide evidence that CRAFT uses its capacity to model conversational context in an order-sensitive fashion, and that it makes effective use of the dynamics within. An important avenue for future work would be developing more transparent models that can shed light on exactly what kinds of order-related features are being extracted and how they are used in prediction.
Conclusions and Future Work
In this work, we introduced a model for forecasting conversational events that processes comments as they happen and takes the full conversational context into account to make an updated prediction at each step. This model fills a void in the existing literature on conversational forecasting, simultaneously addressing the dual challenges of capturing inter-comment dynamics and dealing with an unknown horizon. We find that our model achieves state-of-the-art performance on the task of forecasting derailment in two different datasets that we release publicly. We further show that the resulting system can provide substantial prior notice of derailment, opening up the potential for preemptive interventions by human moderators BIBREF65.
While we have focused specifically on the task of forecasting derailment, we view this work as a step towards a more general model for real-time forecasting of other types of emergent properties of conversations. Follow-up work could adapt the CRAFT architecture to address other forecasting tasks mentioned in Section SECREF2—including those for which the outcome is extraneous to the conversation. We expect different tasks to be informed by different types of inter-comment dynamics, and further architecture extensions could add additional supervised fine-tuning in order to direct it to focus on specific dynamics that might be relevant to the task (e.g., exchange of ideas between interlocutors or stonewalling).
With respect to forecasting derailment, there remain open questions regarding what human moderators actually desire from an early-warning system, which would affect the design of a practical system based on this work. For instance, how early does a warning need to be in order for moderators to find it useful? What is the optimal balance between precision, recall, and false positive rate at which such a system is truly improving moderator productivity rather than wasting their time through false positives? What are the ethical implications of such a system? Follow-up work could run a user study of a prototype system with actual moderators to address these questions.
A practical limitation of the current analysis is that it relies on balanced datasets, while derailment is a relatively rare event for which a more restrictive trigger threshold would be appropriate. While our analysis of the precision-recall curve suggests the system is robust across multiple thresholds ($AUPR=0.7$), additional work is needed to establish whether the recall tradeoff would be acceptable in practice.
Finally, one major limitation of the present work is that it assigns a single label to each conversation: does it derail or not? In reality, derailment need not spell the end of a conversation; it is possible that a conversation could get back on track, suffer a repeat occurrence of antisocial behavior, or any number of other trajectories. It would be exciting to consider finer-grained forecasting of conversational trajectories, accounting for the natural—and sometimes chaotic—ebb-and-flow of human interactions.
Acknowledgements. We thank Caleb Chiam, Liye Fu, Lillian Lee, Alexandru Niculescu-Mizil, Andrew Wang and Justine Zhang for insightful conversations (with unknown horizon), Aditya Jha for his great help with implementing and running the crowd-sourcing tasks, Thomas Davidson and Claire Liang for exploratory data annotation, as well as the anonymous reviewers for their helpful comments. This work is supported in part by the NSF CAREER award IIS-1750615 and by the NSF Grant SES-1741441.
Please answer the question based on the text provided above. What are two datasets model is applied to?

Answer:  `Conversations Gone Awry' dataset, subreddit ChangeMyView An expanded version of the existing 'Conversations Gone Awry' dataset and the ChangeMyView dataset, a subreddit whose only annotation is whether the conversation required action by the Reddit moderators.