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題名 基於多數決的不當訊息分析學習系統
A majority-based learning system for analyzing misinformation
作者 高翰君
Kao, Han-Chun
貢獻者 杜雨儒
Tu, Yu-Ju
高翰君
Kao, Han-Chun
關鍵詞 不當訊息
資訊系統
機器學習
假新聞
業配文
原生廣告
抄襲文
Misinformation
Information system
machine learning
fake news
advertorial
native advertising
plagiarism
日期 2021
上傳時間 4-Aug-2021 14:46:35 (UTC+8)
摘要 自過去的十年以來，不當訊息的問題引起了人們的廣泛關注。 直到最近，這個問題變得比以往更具挑戰性，其中一原因來自於covid-19大流行在世界各地蔓延。 在這項研究中，我們表明不當訊息是由三個主要部分構成的：假新聞、業配文和抄襲文。 此外，本研究提出了一種系統，透過整合多種機器學習方法的優勢，以提高不當訊息自主檢測的性能。
Since the past decade, the problem of misinformation has drawn considerable attention. Recently, this problem becomes much more challenging, largely because the covid-19 pandemic unfortunately spread around the world. In this study, we show that misinformation is constructed by three main components: fake news, advertorial and plagiarism. Furthermore, we propose a system to combine the strengths of multiple machine learning approaches to improve the performance of autonomous detection of misinformation.
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3. Akhtar, M. S., Ekbal, A., Narayan, S., Singh, V., & Cambria, E. (2018). No, that never happened!! Investigating rumors on Twitter. IEEE Intelligent Systems, 33(5), 8-15.
4. Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-36.
5. Alzahrani, S. M., Salim, N., & Abraham, A. (2011). Understanding plagiarism linguistic patterns, textual features, and detection methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(2), 133-149.
6. Atallah, R., & Al-Mousa, A. (2019, October). Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method. In 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS) (pp. 1-6). IEEE.
7. Bakker, P. (2012). Aggregation, content farms and Huffinization: The rise of low-pay and no-pay journalism. Journalism practice, 6(5-6), 627-637.
8. Bär, D., Zesch, T., & Gurevych, I. (2012). Text reuse detection using a composition of text similarity measures. Proceedings of COLING 2012, 167-184.
9. Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Science vs conspiracy: Collective narratives in the age of misinformation. PloS one, 10(2), e0118093.
10. Bhagavatula, S., Dunn, C., Kanich, C., Gupta, M., & Ziebart, B. (2014, November). Leveraging machine learning to improve unwanted resource filtering. In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (pp. 95-102).
11. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
12. Burrows, S., Potthast, M., & Stein, B. (2013). Paraphrase acquisition via crowdsourcing and machine learning. ACM Transactions on Intelligent Systems and Technology (TIST), 4(3), 1-21.
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17. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
18. Chitra, A., & Rajkumar, A. (2016). Plagiarism detection using machine learning-based paraphrase recognizer. Journal of Intelligent Systems, 25(3), 351-359.
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21. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554-559.
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26. Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104.
27. Fix, E., & Hodges Jr, J. L. (1952). Discriminatory analysis-nonparametric discrimination: Small sample performance.
28. Ellerbach, J. (2004). The advertorial as information pollution. Journal of information ethics, 61.
29. Fake news. (2020) In OED Online, Oxford University Press. Retrieved from https://www.oed.com/view/Entry/67776.
30. Frank, T. (2011). Bright Frenetic Mills. Will the Last Reporter Turn Out the Lights, ed. Robert W. McChesney and Victor Pickard (New York: The New Press, 2011), 114.
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33. Gilda, S. (2017, December). Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th Student Conference on Research and Development (SCOReD) (pp. 110-115). IEEE.
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35. Helmstetter, S., & Paulheim, H. (2018, August). Weakly supervised learning for fake news detection on Twitter. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 274-277). IEEE.
36. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
37. Janze, C., & Risius, M. (2017). Automatic Detection of Fake News on Social Media Platforms. PACIS, 261.
38. Jin, F., Dougherty, E., Saraf, P., Cao, Y., & Ramakrishnan, N. (2013, August). Epidemiological modeling of news and rumors on twitter. In Proceedings of the 7th workshop on social network mining and analysis (pp. 1-9).
39. Jin, Z., Cao, J., Zhang, Y., Zhou, J., & Tian, Q. (2016). Novel visual and statistical image features for microblogs news verification. IEEE transactions on multimedia, 19(3), 598-608.
40. Kim, B. H., Pasadeos, Y., & Barban, A. (2001). On the deceptive effectiveness of labeled and unlabeled advertorial formats. Mass Communication & Society, 4(3), 265-281.
41. Kwon, S., Cha, M., & Jung, K. (2017). Rumor detection over varying time windows. PloS one, 12(1), e0168344.
42. Lam, L., & Suen, C. Y. (1995). Optimal combinations of pattern classifiers. Pattern Recognition Letters, 16(9), 945-954.
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44. Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009, June). Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1245-1254).
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描述 碩士
國立政治大學
資訊管理學系
108356004
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108356004
資料類型 thesis
dc.contributor.advisor 杜雨儒zh_TW
dc.contributor.advisor Tu, Yu-Juen_US
dc.contributor.author (Authors) 高翰君zh_TW
dc.contributor.author (Authors) Kao, Han-Chunen_US
dc.creator (作者) 高翰君zh_TW
dc.creator (作者) Kao, Han-Chunen_US
dc.date (日期) 2021en_US
dc.date.accessioned 4-Aug-2021 14:46:35 (UTC+8)-
dc.date.available 4-Aug-2021 14:46:35 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2021 14:46:35 (UTC+8)-
dc.identifier (Other Identifiers) G0108356004en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136339-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 108356004zh_TW
dc.description.abstract (摘要) 自過去的十年以來，不當訊息的問題引起了人們的廣泛關注。 直到最近，這個問題變得比以往更具挑戰性，其中一原因來自於covid-19大流行在世界各地蔓延。 在這項研究中，我們表明不當訊息是由三個主要部分構成的：假新聞、業配文和抄襲文。 此外，本研究提出了一種系統，透過整合多種機器學習方法的優勢，以提高不當訊息自主檢測的性能。zh_TW
dc.description.abstract (摘要) Since the past decade, the problem of misinformation has drawn considerable attention. Recently, this problem becomes much more challenging, largely because the covid-19 pandemic unfortunately spread around the world. In this study, we show that misinformation is constructed by three main components: fake news, advertorial and plagiarism. Furthermore, we propose a system to combine the strengths of multiple machine learning approaches to improve the performance of autonomous detection of misinformation.en_US
dc.description.tableofcontents 1. Introduction 5
1.1 Background 5
1.2 Research questions 6
2. Literature review 8
2.1 Definition of misinformation 8
2.2 The case of content farms and the quality of information 10
2.3 Fake news 12
2.4 Plagiarism 20
2.5 Advertorial and native advertising content 22
2.6 Related studies of misinformation detection 25
3. Proposed model 31
4. Computational experiments 36
5. Summary of findings 39
6. Conclusions 42
References 43
Appendix 49		zh_TW
dc.format.extent 2525782 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356004en_US
dc.subject (關鍵詞) 不當訊息zh_TW
dc.subject (關鍵詞) 資訊系統zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 假新聞zh_TW
dc.subject (關鍵詞) 業配文zh_TW
dc.subject (關鍵詞) 原生廣告zh_TW
dc.subject (關鍵詞) 抄襲文zh_TW
dc.subject (關鍵詞) Misinformationen_US
dc.subject (關鍵詞) Information systemen_US
dc.subject (關鍵詞) machine learningen_US
dc.subject (關鍵詞) fake newsen_US
dc.subject (關鍵詞) advertorialen_US
dc.subject (關鍵詞) native advertisingen_US
dc.subject (關鍵詞) plagiarismen_US
dc.title (題名) 基於多數決的不當訊息分析學習系統zh_TW
dc.title (題名) A majority-based learning system for analyzing misinformationen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Advertorial. (2020) In OED Online, Oxford University Press. Retrieved from www.oed.com/view/Entry/2983.
2. Ahmed, H., Traore, I., & Saad, S. (2018). Detecting opinion spams and fake news using text classification. Security and Privacy, 1(1), e9.
3. Akhtar, M. S., Ekbal, A., Narayan, S., Singh, V., & Cambria, E. (2018). No, that never happened!! Investigating rumors on Twitter. IEEE Intelligent Systems, 33(5), 8-15.
4. Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-36.
5. Alzahrani, S. M., Salim, N., & Abraham, A. (2011). Understanding plagiarism linguistic patterns, textual features, and detection methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(2), 133-149.
6. Atallah, R., & Al-Mousa, A. (2019, October). Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method. In 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS) (pp. 1-6). IEEE.
7. Bakker, P. (2012). Aggregation, content farms and Huffinization: The rise of low-pay and no-pay journalism. Journalism practice, 6(5-6), 627-637.
8. Bär, D., Zesch, T., & Gurevych, I. (2012). Text reuse detection using a composition of text similarity measures. Proceedings of COLING 2012, 167-184.
9. Bessi, A., Coletto, M., Davidescu, G. A., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Science vs conspiracy: Collective narratives in the age of misinformation. PloS one, 10(2), e0118093.
10. Bhagavatula, S., Dunn, C., Kanich, C., Gupta, M., & Ziebart, B. (2014, November). Leveraging machine learning to improve unwanted resource filtering. In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (pp. 95-102).
11. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
12. Burrows, S., Potthast, M., & Stein, B. (2013). Paraphrase acquisition via crowdsourcing and machine learning. ACM Transactions on Intelligent Systems and Technology (TIST), 4(3), 1-21.
13. Cameron, G. T., & Ju-Pak, K. H. (2000). Information pollution?: Labeling and format of advertorials. Newspaper Research Journal, 21(1), 65-76.
14. Cameron, G. T., Ju-Pak, K. H., & Kim, B. H. (1996). Advertorials in magazines: Current use and compliance with industry guidelines. Journalism & Mass Communication Quarterly, 73(3), 722-733.
15. Campbell, C., & Marks, L. J. (2015). Good native advertising isn’ta secret. Business Horizons, 58(6), 599-606.
16. Castillo, C., Mendoza, M., & Poblete, B. (2011, March). Information credibility on twitter. In Proceedings of the 20th international conference on World wide web (pp. 675-684).
17. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
18. Chitra, A., & Rajkumar, A. (2016). Plagiarism detection using machine learning-based paraphrase recognizer. Journal of Intelligent Systems, 25(3), 351-359.
19. Clough, P. (2003). Old and new challenges in automatic plagiarism detection. In National Plagiarism Advisory Service, 2003; http://ir. shef. ac. uk/cloughie/index. html.
20. Dadgar, S. M. H., Araghi, M. S., & Farahani, M. M. (2016, March). A novel text mining approach based on TF-IDF and Support Vector Machine for news classification. In 2016 IEEE International Conference on Engineering and Technology (ICETECH) (pp. 112-116). IEEE.
21. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., ... & Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554-559.
22. Disinformation. (2020) In Cambridge dictionary, Cambridge University Press. Retrieved from https://dictionary.cambridge.org/us/dictionary/english/disinformation
23. Doerr, B., Fouz, M., & Friedrich, T. (2012). Why rumors spread so quickly in social networks. Communications of the ACM, 55(6), 70-75.
24. Drucker, H., Burges, C. J., Kaufman, L., Smola, A. J., & Vapnik, V. (1997). Support vector regression machines. Advances in neural information processing systems, 155-161.
25. Eiselt, M. P. B. S. A., & Rosso, A. B. C. P. (2009). Overview of the 1st international competition on plagiarism detection. In 3rd PAN Workshop. Uncovering Plagiarism, Authorship and Social Software Misuse (p. 1).
26. Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104.
27. Fix, E., & Hodges Jr, J. L. (1952). Discriminatory analysis-nonparametric discrimination: Small sample performance.
28. Ellerbach, J. (2004). The advertorial as information pollution. Journal of information ethics, 61.
29. Fake news. (2020) In OED Online, Oxford University Press. Retrieved from https://www.oed.com/view/Entry/67776.
30. Frank, T. (2011). Bright Frenetic Mills. Will the Last Reporter Turn Out the Lights, ed. Robert W. McChesney and Victor Pickard (New York: The New Press, 2011), 114.
31. Friggeri, A., Adamic, L., Eckles, D., & Cheng, J. (2014, May). Rumor cascades. In Eighth international AAAI conference on weblogs and social media.
32. Gelfert, A. (2018). Fake news: A definition. Informal Logic, 38(1), 84-117.
33. Gilda, S. (2017, December). Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th Student Conference on Research and Development (SCOReD) (pp. 110-115). IEEE.
34. Gupta, A., Lamba, H., Kumaraguru, P., & Joshi, A. (2013, May). Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In Proceedings of the 22nd international conference on World Wide Web (pp. 729-736).
35. Helmstetter, S., & Paulheim, H. (2018, August). Weakly supervised learning for fake news detection on Twitter. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 274-277). IEEE.
36. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
37. Janze, C., & Risius, M. (2017). Automatic Detection of Fake News on Social Media Platforms. PACIS, 261.
38. Jin, F., Dougherty, E., Saraf, P., Cao, Y., & Ramakrishnan, N. (2013, August). Epidemiological modeling of news and rumors on twitter. In Proceedings of the 7th workshop on social network mining and analysis (pp. 1-9).
39. Jin, Z., Cao, J., Zhang, Y., Zhou, J., & Tian, Q. (2016). Novel visual and statistical image features for microblogs news verification. IEEE transactions on multimedia, 19(3), 598-608.
40. Kim, B. H., Pasadeos, Y., & Barban, A. (2001). On the deceptive effectiveness of labeled and unlabeled advertorial formats. Mass Communication & Society, 4(3), 265-281.
41. Kwon, S., Cha, M., & Jung, K. (2017). Rumor detection over varying time windows. PloS one, 12(1), e0168344.
42. Lam, L., & Suen, C. Y. (1995). Optimal combinations of pattern classifiers. Pattern Recognition Letters, 16(9), 945-954.
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dc.identifier.doi (DOI) 10.6814/NCCU202100686en_US