SlowGuess/NAACL_2021_OCR huggingface dataset:
Table 1: Network analysis and statistics
Type of Context	Model	M. F1 ↑	Recalls ↑
Non-Contextual	RF+TF	0.536	0.513
CLSTM	0.588	0.597
Historical Context	CCNN	0.729	0.587
SDM	0.743	0.755†
DualContextBERT	0.767	0.786†
STATENet	0.799*†	0.810*†
Social Context	SNAPBATNET	0.776*	0.606
Social + Historical	HyperSOS	0.792*†	0.818*†
Table 2: Mean of results obtained over 10 runs. * indicates that the result is significantly  $(p <   0.005)$  better than DualContextBert and  $\dagger$  represents better than SNAPBATNET under Wilcoxon's Signed Rank test). Bold indicates best performance.
Contextual vs. Non-Contextual Models: We compare Hyper-SOS with a variety of models in Table 2. We categorize the models as non-contextual, i.e., using the current tweet only, and more recent user-contextual, spanning both social and historical context. We note that user-contextual models drastically outperform RF+TF and LSTM that only leverage the language of the tweet without any additional user context. We attribute these improvements to the ability of personally contextual models to better ascertain a user's mental state through their historical activity and communities they interact with (Flek, 2020).
Contextual Models: Amongst models utilizing user's historical tweeting activity, we note methods modeling user tweets as temporal sequence (DualContextBERT, Hyper-SOS) outperform bag-of-tweets based models (C-CNN, SDM). On the other hand, prior work leveraging shallow features from social graph's structure without any temporal context (SNAPBATNET), is competitive to historical context models. This sets the premise for leveraging user's social relations as shallow features in neural methods, validating the effectiveness of social context for suicide ideation detection. HyperSOS significantly  $(p < 0.005)$  outperforms both
social and historical contextual models, by virtue of its design. Hyper-SOS's design captures the scale-free nature of social relations through deep graph convolutions that blend language features across a user's historical tweeting activity to ascertain suicide ideation. These results validate the potential of utilizing social and historical context, as reflected in psychological works discussing the interpersonal theory of suicide (Joiner, 2007, 2009; Ordern et al., 2010). The higher Macro F1 of STATENet can be attributed to its compute-intensive, learnable historical modeling component. We leave using a learnable model to encode personal historical context to our future research directions.
Hyper-SOS advances prior work on multiple fronts: i) combining social and historical context, ii) deep graph convolutions rather than shallow structural features, iii) capturing the scale-free nature of social networks through hyperbolic transformation, and iv) modeling a user's emotions based on the HEAT Mechanism. We explore the impact of each of these design choices through a series of ablative and exploratory analyses next.
6.2 Hyper-SOS Ablation Study
Figure 5: Confidence intervals for evaluation metrics of Ablation study over 10 different runs.  $(p)$  indicates the p-value under Wilcoxon's Signed Rank test.
We analyze Hyper-SOS's components through an ablation study in Figure 5. We start by examining how the predictive power of the base (CurrentTweet) model changes when enriched with user's historical emotional context (HEAT), then gradually with social context (GCN), and finally on adding hyperbolic transformations over graph convolutions (Hyper-SOS). We note that incorporating a user's historical emotion spectrum via HEAT in a time-sensitive manner improves performance. Specifically, we note improvement in recall in terms of correctly identifying the presence
of suicide ideation, likely due to the contextualization of a user's mental state via temporal context.
We note significant  $(p < 0.05)$  improvements by leveraging social context, learning representations through feature aggregations within a user's neighborhood. These aggregations enrich the learned representations through the structure and historical emotion-based features of the communities the user interacts with, further amplifying the predictive power by greater contextualization.
Lastly, building on the scale-free nature of social networks (Cox et al., 2012), leveraging feature transformations and graph convolutions in the hyperbolic space brings further improvements, as plain GCNs are unable to generalize over such hierarchical scale-free structures (Fronczak, 2018). Our observations revalidate the utility of HyperSOS for suicide ideation detection, specifically the influence of social context, and correctly capturing the network's scale-free traits (Rosenquist et al., 2011).
6.3 Impact of Historical Context Aggregation
(a) Other temporal functions
(b) HEAT saturation
Figure 6: F1 changes with (a) other temporal user embeddings and (b) different temporal window (10 runs).
We analyze Hyper-SOS's sensitivity to the choice of temporal kernels for aggregating user's historical tweets as shown in Figure 6a. Overall, we notice that all user features learned via temporal aggregations outperform the CurrentTweet representation that does not use any historical information. The temporal kernels' performance improves as we factor in more historical tweets up to a year. We also find that Linear Decay performs better than Exponential Decay, hinting towards the importance of older tweets ( $>3$  months), in some cases, for contextualizing user's more recent suicide ideation with past emotional states.
We note that using the HEAT mechanism as a temporal kernel consistently bestows significant improvements in Hyper-SOS's performance over time compared to all other variants. Self-exciting
Figure 7: We study four users in a social graph, with their tweet to be assessed, historical tweets, and timestamps. The social graph shows the four users and their interactions among themselves and other users. We also show aggregated historical emotions through HEAT mechanism over time.
temporal point processes such as the Hawkes mechanism have shown great promise in modeling social media dynamics (Rizoiu et al., 2017) and user behavior over time (Guo et al., 2019), revalidating the effectiveness of our proposed HEAT mechanism for learning user representations. Further, we note that Hyper-SOS's performance saturates on adding history beyond a year (Figure 6b). This is in line with psychology research, noting the depreciating importance of user's emotions over longer time periods (Selby et al., 2013; Kaplow et al., 2014; Glenn et al., 2020).
6.4 Impact of Different User Relations
Relation Type	Macro F1↑	Recall for SI↑
All (Hyper-SoS)	0.792*	0.818*
(×) Mentions	0.774	0.771
(×) Quotes	0.780	0.804
(×) ReplyTo	0.776	0.802
Table 3: Mean of performance by removing each type of relation from the social network graph obtained over 10 runs. Result with all relations is significantly better than with any relation type removed.
We analyze the importance of different types of social network relations based on how two users interact, by removing each relation type from the graph, as shown in Table 3. We note that removing relations based on user mentions, Hyper-SOS's performance drastically drops. We postulate this drop to the physical and cognitive effort a mention requires, as opposed to other forms of user
interactions, in fact, it is the strongest form of user interaction on Twitter (Fink et al., 2016). This observation aligns with the findings of prior social network research that explore the influence of different communications on Twitter (Grabowicz et al., 2012), especially in networks where users can be influenced by a few "known" users (Cha et al., 2010). We note that relatively weaker forms of interactions such as quotes and replies do not contribute towards social context as much as mentions for suicide ideation detection. As suggested in past studies (Sultana et al., 2017), we observe that combining all the user interactions significantly  $(p < 0.005)$  improves Hyper-SOS's performance.
6.5 Exploratory and Error Analysis
We now present a qualitative analysis (Figure 7) to derive deeper insights into Hyper-SOS's predictive power. We see that the most recent tweet by user A shows explicit signs of suicidal intent. However, from their historical tweets, we notice that User A is talking about their gaming experience. Hence, studying the tweet to be assessed in isolation is not sufficient to assess users' risk, even for humans. Indeed, only temporally contextual models (HEAT, Hyper-SOS) correctly predict the absence of suicidal intent. In a more challenging case, that of user B, the tweet to be assessed shows no overt signs of suicidal intent, and their historical activity is not concerning either. Hyper-SOS's graph-based learning alleviates this issue by learning from a user's social context. Upon analyzing the network, we note User B's interaction with user C's tweets, which
are suicidal, which might influence the tendency of User B to show suicidal behavior. Moreover, user C is a highly connected, influential node and has the potential to impress the emotions of users who interact with it (Chung and Zeng, 2020). User D presents an error case. We find that the tweet to be assessed is ambiguous, and historical activity is not informative either. Moreover, user D is isolated, highlighting that suicide ideation detection in the absence of contextual elements (historical activity, network interactions) can be highly subjective, and paves the way for future work.
7 Broader Impact and Ethics
Emphasizing the sensitive nature of this work, we acknowledge the trade-off between privacy and effectiveness (Eskisabel-Azpiazu et al., 2017). To avoid coercion and intrusive treatment, we work within the purview of acceptable privacy practices suggested by Chancellor et al. (2019b) and considerations discussed by Fiesler and Proferes (2018). Although informed consent of each user was not sought as it may be deemed coercive, we perform automatic de-identification of the dataset using named entity recognition (Benton et al., 2017a,b) to reduce the risk of including any identifying data in the raw data. We paraphrase all examples shown in this work to protect user privacy (Chancellor et al., 2019a,b). All the user data is kept separately on protected servers linked to the raw text and network data only through anonymous IDs.
We acknowledge that it is almost impossible to prevent abuse of released technology even when developed with good intentions (Jonas, 1984; Hovy and Spruit, 2016). Hence, we ensure that this analysis is shared only selectively and subject to IRB approval (Zimmer, 2009, 2010) to avoid misuse such as Samaritan's Radar (Hsin et al., 2016).
Limitations: We acknowledge that suicidality is subjective (Keilp et al., 2012), the interpretation of this analysis may vary across individuals on social media (Puschman, 2017), and we do not know the true intentions of the user behind the post. We further acknowledge that suicide risk exists on a diverse spectrum (Bryan and Rudd, 2006), and a binary distinction is a task simplification intended to alert the human in the loop about exceeding a possible intervention threshold. We also recognize that the studied data may be susceptible to demographic, annotator, and medium-specific biases (Hovy and Spruit, 2016).
Future Practical Applicability In the future, we would want to focus on creating a differentially private public model that can be shared with the community while preserving user privacy (Lyu et al., 2020; Yu et al., 2019). Further, suicide ideation detection on social media can involve failure modes that could potentially incorrectly ascertain suicide risk. To this end, we focus on Hyper-SOS as a preliminary tool for prioritizing human expert, clinical psychologist-based assessment.
Motivated by psychological studies, we propose a framework jointly leveraging emotional history from user's past tweets and social information from user's neighborhood in a network to contextualize the interpretation of the latest tweet of a user. To our knowledge, this is the first deep graph neural network study to automatically identify suicide ideation on social media. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, and demonstrate that these are more suitable for our Twitter task than their euclidean counterparts. Inspired by geophysics, we further propose the use of HEAT Mechanism to learn the historic emotional spectrum of a user in a time-sensitive manner. When analyzing the contributions of its individual components to assess suicidal intent, we demonstrate the beneficial impact of both the social and personal context representations.
This work has been supported by the German Federal Ministry of Education and Research (BMBF) as a part of the Junior AI Scientists program under the reference 01-S20060. We thank the anonymous reviewers for their valuable input.
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