Understanding how social discussion platforms like Reddit are influencing financial behavior (2403.04298v2)
Abstract: This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Dirichlet Allocation, we propose a few measures to assess user participation, influence and topic affinities specifically. Our study also maps user-generated content to components of behavioral finance. While these types of information are usually gathered through surveys, it is obvious that large scale data analysis from social media can reveal many potentially unknown or rare insights. Characterising users based on their platform behavior to provide critical insights about how communities are formed and trust is established in these platforms using graphical analysis is also studied.
- H. Yanto, N. Ismail, K. Kiswanto, N. M. Rahim, and N. Baroroh, “The roles of peers and social media in building financial literacy among the millennial generation: A case of indonesian economics and business students,” Cogent Social Sciences, vol. 7, no. 1, p. 1947579, 2021.
- G. Isomidinova, J. S. K. Singh, and K. Singh, “Determinants of financial literacy: a quantitative study among young students in tashkent, uzbekistan,” Electronic Journal of Business & Management, vol. 2, no. 1, pp. 61–75, 2017.
- P. R. Andarsari and M. N. Ningtyas, “The role of financial literacy on financial behavior,” Journal of accounting and business education, vol. 4, no. 1, pp. 24–33, 2019.
- Pushshift, “Calling API for dumping dataset,” https://pushshift.io/api-parameters, 2021, accessed: 2021-01-01.
- R. Rehurek and P. Sojka, “Gensim–python framework for vector space modelling,” NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic, vol. 3, no. 2, p. 2, 2011.
- L. Yao, D. Mimno, and A. McCallum, “Efficient methods for topic model inference on streaming document collections,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 937–946.
- E. Fast, B. Chen, and M. S. Bernstein, “Empath: Understanding topic signals in large-scale text,” in Proceedings of the 2016 CHI conference on human factors in computing systems, 2016, pp. 4647–4657.
- N. Oliveira, P. Cortez, and N. Areal, “The impact of microblogging data for stock market prediction: Using twitter to predict returns, volatility, trading volume and survey sentiment indices,” Expert Systems with applications, vol. 73, pp. 125–144, 2017.
- M. A. Sattar, M. Toseef, and M. F. Sattar, “Behavioral finance biases in investment decision making,” International Journal of Accounting, Finance and Risk Management, vol. 5, no. 2, p. 69, 2020.
- D. E. Froehlich and J. Brouwer, “Social network analysis as mixed analysis,” in The Routledge Reviewer’s Guide to Mixed Methods Analysis. Routledge, 2021, pp. 209–218.
- H. Zogan, I. Razzak, S. Jameel, and G. Xu, “Depressionnet: A novel summarization boosted deep framework for depression detection on social media,” arXiv preprint arXiv:2105.10878, 2021.
- D. E. Losada, F. Crestani, and J. Parapar, “erisk 2020: Self-harm and depression challenges,” Advances in Information Retrieval, vol. 12036, p. 557, 2020.
- E. A. Ríssola, D. E. Losada, and F. Crestani, “A survey of computational methods for online mental state assessment on social media,” ACM Transactions on Computing for Healthcare, vol. 2, no. 2, pp. 1–31, 2021.
- H. Yin, S. Yang, and J. Li, “Detecting topic and sentiment dynamics due to Covid-19 pandemic using social media,” in International Conference on Advanced Data Mining and Applications. Springer, 2020, pp. 610–623.
- D. Prabhakar Kaila, D. A. Prasad et al., “Informational flow on twitter–corona virus outbreak–topic modelling approach,” International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 11, no. 3, 2020.
- V. Karpenko, K. Mukhina, D. Rybakova, I. Busurkina, and D. Bulygin, “A study of personal finance practices. the case of online discussions on reddit.” in IMS, 2021, pp. 206–211.
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