Detecting Online Risks and Supportive Interactions for Mobile Intervention and Parental Control Apps
Authors: Ondrej Sotolar, Jaromír Plhák, David Smahel


Reviewer paper summaries: The paper compares different models that detect common positive and negative influence factors in adolescents' well-being in instant messaging apps. The paper uses small-size discriminative and generative classifiers that can work on smartphones/ mobile devices. The paper also collects instant message conversations from Czech volunteer adolescents and creates an annotated corpus.
Summary of strengths: - The paper creates a large-scale dataset for a domain where such data creation is difficult. 
- The paper focuses on an active area of high-impact research.
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Summary of weaknesses: - The language around the parental control apps is out of place. Refer to transitions at lines 84 to 88

- I may be assuming things, but the verbatim around using smaller language models for resource constraints seems to be motivated by the choice of models rather than the choice of models being motivated by the resource constraints. There are no descriptions about which resource constraints occur in nature, other attempts to deploy small models, there are no performance comparisons to larger models to show drops in performance, and there are no graphs that show a trade-off between performance and size. Furthermore, API latency vs on-device inference latencies can be compared.

- Dialogs are called complex psychological phenomena. Most languages can be characterized as Complex Psychological Phenomena.

- The model selection set is arbitrary (there are many smaller similarly sized models. The paper would benefit from a comparative analysis.

- Ethical issues: The authors claim that they went above and beyond to ensure proper ethical conduct was followed. In the next version, can you please describe the steps that you took for the same?
Comments, suggestions and typos: NA
Overall assessment scores: 2