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1. An Investigation on the Portrayal of Blue Whale Challenge on YouTube and Twitter

   https://doi.org/10.1177/1071181319631179  

   Khasawneh, Amro ; Chalil Madathil, Kapil ; Dixon, Emma ; Wisniewski, Pamela ; Zinzow, Heidi ; Roth, Rebecca ( November 2019 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Introduction Social media has created opportunities for children to gather social support online (Blackwell et al., 2016; Gonzales, 2017; Jackson, Bailey, & Foucault Welles, 2018; Khasawneh, Rogers, Bertrand, Madathil, & Gramopadhye, 2019; Ponathil, Agnisarman, Khasawneh, Narasimha, & Madathil, 2017). However, social media also has the potential to expose children and adolescents to undesirable behaviors. Research showed that social media can be used to harass, discriminate (Fritz & Gonzales, 2018), dox (Wood, Rose, & Thompson, 2018), and socially disenfranchise children (Page, Wisniewski, Knijnenburg, & Namara, 2018). Other research proposes that social media use might be correlated to the significant increase in suicide rates and depressive symptoms among children and adolescents in the past ten years (Mitchell, Wells, Priebe, & Ybarra, 2014). Evidence based research suggests that suicidal and unwanted behaviors can be promulgated through social contagion effects, which model, normalize, and reinforce self-harming behavior (Hilton, 2017). These harmful behaviors and social contagion effects may occur more frequently through repetitive exposure and modelling via social media, especially when such content goes “viral” (Hilton, 2017). One example of viral self-harming behavior that has generated significant media attention is the Blue Whale Challenge (BWC). The hearsay about this challenge is that individuals at all ages are persuaded to participate in self-harm and eventually kill themselves (Mukhra, Baryah, Krishan, & Kanchan, 2017). Research is needed specifically concerning BWC ethical concerns, the effects the game may have on teenagers, and potential governmental interventions. To address this gap in the literature, the current study uses qualitative and content analysis research techniques to illustrate the risk of self-harm and suicide contagion through the portrayal of BWC on YouTube and Twitter Posts. The purpose of this study is to analyze the portrayal of BWC on YouTube and Twitter in order to identify the themes that are presented on YouTube and Twitter posts that share and discuss BWC. In addition, we want to explore to what extent are YouTube videos compliant with safe and effective suicide messaging guidelines proposed by the Suicide Prevention Resource Center (SPRC). Method Two social media websites were used to gather the data: 60 videos and 1,112 comments from YouTube and 150 posts from Twitter. The common themes of the YouTube videos, comments on those videos, and the Twitter posts were identified using grounded, thematic content analysis on the collected data (Padgett, 2001). Three codebooks were built, one for each type of data. The data for each site were analyzed, and the common themes were identified. A deductive coding analysis was conducted on the YouTube videos based on the nine SPRC safe and effective messaging guidelines (Suicide Prevention Resource Center, 2006). The analysis explored the number of videos that violated these guidelines and which guidelines were violated the most. The inter-rater reliabilities between the coders ranged from 0.61 – 0.81 based on Cohen’s kappa. Then the coders conducted consensus coding. Results & Findings Three common themes were identified among all the posts in the three social media platforms included in this study. The first theme included posts where social media users were trying to raise awareness and warning parents about this dangerous phenomenon in order to reduce the risk of any potential participation in BWC. This was the most common theme in the videos and posts. Additionally, the posts claimed that there are more than 100 people who have played BWC worldwide and provided detailed description of what each individual did while playing the game. These videos also described the tasks and different names of the game. Only few videos provided recommendations to teenagers who might be playing or thinking of playing the game and fewer videos mentioned that the provided statistics were not confirmed by reliable sources. The second theme included posts of people that either criticized the teenagers who participated in BWC or made fun of them for a couple of reasons: they agreed with the purpose of BWC of “cleaning the society of people with mental issues,” or they misunderstood why teenagers participate in these kind of challenges, such as thinking they mainly participate due to peer pressure or to “show off”. The last theme we identified was that most of these users tend to speak in detail about someone who already participated in BWC. These videos and posts provided information about their demographics and interviews with their parents or acquaintances, who also provide more details about the participant’s personal life. The evaluation of the videos based on the SPRC safe messaging guidelines showed that 37% of the YouTube videos met fewer than 3 of the 9 safe messaging guidelines. Around 50% of them met only 4 to 6 of the guidelines, while the remaining 13% met 7 or more of the guidelines. Discussion This study is the first to systematically investigate the quality, portrayal, and reach of BWC on social media. Based on our findings from the emerging themes and the evaluation of the SPRC safe messaging guidelines we suggest that these videos could contribute to the spread of these deadly challenges (or suicide in general since the game might be a hoax) instead of raising awareness. Our suggestion is parallel with similar studies conducted on the portrait of suicide in traditional media (Fekete & Macsai, 1990; Fekete & Schmidtke, 1995). Most posts on social media romanticized people who have died by following this challenge, and younger vulnerable teens may see the victims as role models, leading them to end their lives in the same way (Fekete & Schmidtke, 1995). The videos presented statistics about the number of suicides believed to be related to this challenge in a way that made suicide seem common (Cialdini, 2003). In addition, the videos presented extensive personal information about the people who have died by suicide while playing the BWC. These videos also provided detailed descriptions of the final task, including pictures of self-harm, material that may encourage vulnerable teens to consider ending their lives and provide them with methods on how to do so (Fekete & Macsai, 1990). On the other hand, these videos both failed to emphasize prevention by highlighting effective treatments for mental health problems and failed to encourage teenagers with mental health problems to seek help and providing information on where to find it. YouTube and Twitter are capable of influencing a large number of teenagers (Khasawneh, Ponathil, Firat Ozkan, & Chalil Madathil, 2018; Pater & Mynatt, 2017). We suggest that it is urgent to monitor social media posts related to BWC and similar self-harm challenges (e.g., the Momo Challenge). Additionally, the SPRC should properly educate social media users, particularly those with more influence (e.g., celebrities) on elements that boost negative contagion effects. While the veracity of these challenges is doubted by some, posting about the challenges in unsafe manners can contribute to contagion regardless of the challlenges’ true nature. 

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2. Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence

   https://doi.org/10.1038/s41398-023-02599-9  

   de Lacy, Nina ; Ramshaw, Michael J. ; McCauley, Elizabeth ; Kerr, Kathleen F. ; Kaufman, Joan ; Nathan Kutz, J. ( October 2023 , Translational Psychiatry)

   Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disruptive behaviors and post-traumatic stress. Our models tested well in unseen, held-out data (AUC ≥ 0.94). By utilizing a large-scale design and advanced computational approaches, we were able to compare the relative predictive ability of neural versus psychosocial features in a principled manner and found that psychosocial features consistently outperformed neural metrics in their relative ability to deliver robust predictions of individual cases. We found that deep learning with artificial neural networks and tree-based learning with XGBoost outperformed logistic regression with ElasticNet, supporting the conceptualization of mental illnesses as multifactorial disease processes with non-linear relationships among predictors that can be robustly modeled with computational psychiatry techniques. To our knowledge, this is the first study to test the relative predictive ability of these gold-standard algorithms from different classes across multiple mental health conditions in youth within the same study design in multidomain data utilizing >100 candidate predictors. Further research is suggested to explore these findings in longitudinal data and validate results in an external dataset.

    

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3. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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4. Roboviz: A Game-Centered Project for Information Visualization Education

   https://doi.org/10.1109/TVCG.2022.3209402  

   Adar, Eytan ; Lee-Robbins, Elsie ( January 2023 , IEEE Transactions on Visualization and Computer Graphics)

   Due to their pedagogical advantages, large final projects in information visualization courses have become standard practice. Students take on a client–real or simulated–a dataset, and a vague set of goals to create a complete visualization or visual analytics product. Unfortunately, many projects suffer from ambiguous goals, over or under-constrained client expectations, and data constraints that have students spending their time on non-visualization problems (e.g., data cleaning). These are important skills, but are often secondary course objectives, and unforeseen problems can majorly hinder students. We created an alternative for our information visualization course: Roboviz, a real-time game for students to play by building a visualization-focused interface. By designing the game mechanics around four different data types, the project allows students to create a wide array of interactive visualizations. Student teams play against their classmates with the objective to collect the most (good) robots. The flexibility of the strategies encourages variability, a range of approaches, and solving wicked design constraints. We describe the construction of this game and report on student projects over two years. We further show how the game mechanics can be extended or adapted to other game-based projects. 

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5. A Review on Strategies for Data Collection, Reflection, and Communication in Eating Disorder Apps

   https://doi.org/10.1145/3411764.3445670  

   Devakumar, Anjali ; Modh, Jay ; Saket, Bahador ; Baumer, Eric P. ; De Choudhury, Munmun ( May 2021 , Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems)

   Eating disorders (EDs) constitute a mental illness with the highest mortality. Today, mobile health apps provide promising means to ED patients for managing their condition. Apps enable users to monitor their eating habits, thoughts, and feelings, and offer analytic insights for behavior change. However, not only have scholars critiqued the clinical validity of these apps, their underlying design principles are not well understood. Through a review of 34 ED apps, we uncovered 11 different data types ED apps collect, and 9 strategies they employ to support collection and reflection. Drawing upon personal health informatics and visualization frameworks, we found that most apps did not adhere to best practices on what and how data should be collected from and reflected to users, or how data-driven insights should be communicated. Our review offers suggestions for improving the design of ED apps such that they can be useful and meaningful in ED recovery. 

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