Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Analyzing Key Users' behavior trends in Volunteer-Based Networks (2310.05978v1)

Published 4 Oct 2023 in cs.SI and cs.LG

Abstract: Online social networks usage has increased significantly in the last decade and continues to grow in popularity. Multiple social platforms use volunteers as a central component. The behavior of volunteers in volunteer-based networks has been studied extensively in recent years. Here, we explore the development of volunteer-based social networks, primarily focusing on their key users' behaviors and activities. We developed two novel algorithms: the first reveals key user behavior patterns over time; the second utilizes machine learning methods to generate a forecasting model that can predict the future behavior of key users, including whether they will remain active donors or change their behavior to become mainly recipients, and vice-versa. These algorithms allowed us to analyze the factors that significantly influence behavior predictions. To evaluate our algorithms, we utilized data from over 2.4 million users on a peer-to-peer food-sharing online platform. Using our algorithm, we identified four main types of key user behavior patterns that occur over time. Moreover, we succeeded in forecasting future active donor key users and predicting the key users that would change their behavior to donors, with an accuracy of up to 89.6%. These findings provide valuable insights into the behavior of key users in volunteer-based social networks and pave the way for more effective communities-building in the future, while using the potential of machine learning for this goal.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (54)
  1. Number of social media users worldwide from 2018 to 2027. https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/. Accessed: 2022-12-13.
  2. Olioex. https://OLIOex.com. Accessed: 2021-01-01.
  3. Moderation practices as emotional labor in sustaining online communities: The case of aapi identity work on reddit. In Proceedings of the 2019 CHI conference on human factors in computing systems, pages 1–13, 2019.
  4. Human-machine collaboration for content regulation: The case of reddit automoderator. ACM Transactions on Computer-Human Interaction (TOCHI), 26(5):1–35, 2019.
  5. Multiple social network learning and its application in volunteerism tendency prediction. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 213–222, 2015.
  6. Volunteerism tendency prediction via harvesting multiple social networks. ACM Transactions on Information Systems (TOIS), 34(2):1–27, 2016.
  7. Volunteers in wikipedia: Why the community matters. Journal of Educational Technology & Society, 13(2):128–140, 2010.
  8. A random forest guided tour. Test, 25:197–227, 2016.
  9. Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. roceedings of the First Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP), pages 79–91, 2020.
  10. Bringing a sharing economy approach into the food sector: The potential of food sharing for reducing food waste. In Food waste reduction and valorisation, pages 197–214. Springer, 2017.
  11. Social and environmental analysis of food waste abatement via the peer-to-peer sharing economy. Journal of nature communication, 2020.
  12. Sofya Aptekar. Gifts among strangers: the social organization of freecycle giving. Social Problems, 63(2):266–283, 2016.
  13. Food waste: Challenges and opportunities for enhancing the emerging bioeconomy. Journal of Cleaner Production, 221:10–16, 2019.
  14. Uncovering the impact of food sharing platform business models: a theory of change approach. British Food Journal, 122, 2019.
  15. Food sharing, redistribution, and waste reduction via mobile applications: A social network analysis. Journal of Industrial Marketing Management, 2019.
  16. Federico Gonzalez Raya. Upscaling collaborative food allocation: The cases of olio, foodsharing, and reko in stockholm, 2021.
  17. Identifying food insecurity in food sharing networks via machine learning. Journal of Business Research, 131:469–484, 2021.
  18. Understanding food sharing models to tackle sustainability challenges. Ecological Economics, 2017.
  19. Digital food sharing and food insecurity in the covid-19 era. Resources, Conservation and Recycling, 189:106735, 2023.
  20. Structural investigation of supply networks: A social network analysis approach. Journal of Operations Management, 29, 2010.
  21. Application of graph theory in social media. JCSEinternational journal of computer sciences and engineering, 2018.
  22. Centrality measures in networks. arXiv preprint arXiv:1608.05845, 2016.
  23. Dr. Julia Heidemann Andrea Landherr, Bettina Friedl. A critical review of centrality measures in social networks. BISE, 2010.
  24. How to fight against food waste in the digital era: Key factors for a successful food sharing platform. journal of Business Research, 124:47–58, 2021.
  25. Community detection in social networks. WIREs Data Mining Knowl Discov2016, 6:115–135, 2016.
  26. Review on community detection algorithms and evaluation measures in social networks. 7th International Conference on Advanced Computing &\&& Communication Systems (ICACCS), 2021.
  27. Discover opinion leader in online social network using firefly algorithm. Expert Systems with Applications, 122:1–15, 2019.
  28. Twitter user clustering based on their preferences and the louvain algorithm. In International Conference on Practical Applications of Agents and Multi-Agent Systems, pages 349–356. Springer, 2016.
  29. Distributed louvain algorithm for graph. IEEE International Parallel and Distributed Processing Symposium, 2018.
  30. Clustering and classification for time series data in visual analytics: A survey. IEEE Access, 7:181314–181338, 2019.
  31. Fu Tak-Chung. A review on time-series data mining. Engineering Applications of Artificial Intelligence, 24, 2011.
  32. A time-series clustering methodology for knowledge extraction in energy consumption data. A time-series clustering methodology for knowledge extraction in energy consumption data, 2020.
  33. The analysis of a simple k-means clustering algorithm. In Proceedings of the sixteenth annual symposium on Computational geometry, pages 100–109, 2000.
  34. A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM Sigkdd Explorations Newsletter, 10(2):12–22, 2008.
  35. Weighted dynamic time warping for time series classification. Pattern recognition, 44(9):2231–2240, 2011.
  36. Estimation of covid-19 dynamics in the different states of the united states using time-series clustering. medRxiv, pages 2020–06, 2020.
  37. Online forecasting of daily feed intake in lactating sows supported by offline time-series clustering, for precision livestock farming. Computers and Electronics in Agriculture, 188:106329, 2021.
  38. Investigation of internal validity measures for k-means clustering. In Proceedings of the international multiconference of engineers and computer scientists, volume 1, pages 14–16. sn, 2012.
  39. Analysis of ego network structure in online social networks. International Conference on Social Computing, 2012.
  40. Investigating community structure in perspective of ego network. Expert Systems with Applications, 42:6913–6934, 2015.
  41. The interaction of size and density with graph-level indices. Social networks, 21(3):239–267, 1999.
  42. Pagerank on semantic networks, with application to word sense disambiguation. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics, pages 1126–1132, 2004.
  43. Zhang Yu and Luo. Degree centrality, betweenness centrality, and closeness centrality in social network. Advances in Intelligent Systems Research, 132, 2017.
  44. Network clustering coefficient without degree-correlation biases. Phisical Revie E71, 057101, 2005.
  45. Daniel Berrar. Bayes’ theorem and naive bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 403, 2018.
  46. Priyanka and Dharmender Kumar. Decision tree classifier: A detailed survey. International Journal of Information and Decision Sciences, 12(3):246–269, 2020.
  47. Walker John. Topics in biostatistics. Topics in Biostatistics, pages 273–303, 2007.
  48. Support vector clustering. Journal of Machine Learning Research, 2:125–137, 2001.
  49. Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4):1–4, 2015.
  50. Feature importance: Opening a soil-transmitted helminth machine learning model via shap. Infectious Disease Modelling, 7(1):262–276, 2022.
  51. Explaining anomalies detected by autoencoders using shap. Journal of Artificial intelligence, 2020.
  52. A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120:70–83, 2018.
  53. Time series clustering and classification. CRC Press, 2019.
  54. Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert systems with applications, 140:112896, 2020.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now