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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150656


    Title: 紛亂的行動者：模擬社群媒體機器人在複雜網絡中對意見極化的影響
    Agents of Discord: Modeling the Impact of Social Bots on Opinion Polarization in Complex Networks
    Authors: 呂修齊
    Lu, Hsiu-Chi
    Contributors: 李宣緯
    陳人豪
    Lee, Hsuan-Wei
    Chen, Jen-Hao
    呂修齊
    Lu, Hsiu-Chi
    Keywords: 社群媒體機器人
    極化
    意見動態
    行動者為本模型
    社會網絡
    Social bots
    Polarization
    Opinion dynamics
    Agent-based model
    Social networks
    Date: 2024
    Issue Date: 2024-04-01 14:21:40 (UTC+8)
    Abstract: 近幾年來，社群媒體機器人的廣泛地存在和對網路的影響已成為相關研究的焦點。過去研究表明，有相當數量在網路上活躍的帳號實際上是由機器人操控，且他們的存在也助長了網路上的意見極化。即便過往已有許多研究與模型談論這些社群媒體機器人對網路社會帶來的負面影響，但目前仍缺乏這些機器人如何透過同時投放於意見連續光譜兩端，造成混亂並導致極化的討論。本研究利用代理人基模型(Agent-based model)，通過進行複雜社交網路的電腦模擬，解釋同時分散在意識光譜兩端的機器人如何加劇社會分歧並造成意見極化。本研究透過改良意見動態模型中的有界信心模型(bounded confidence model)，並將設群媒體機器人行動者投入其中，以研究它們對其他一般人類行動者的影響，進而瞭解它們如何影響了整個網絡的意見動態。
    本研究的主要結果如下：（一）在不同類型的社會網絡中，網絡中的意見分布明顯地隨著部屬於意見光譜兩側的機器人比例而極化。(二)這種極化現象，在網絡中行動者特定的包容度(tolerance)和同質性(homophily)範圍內才可觀察到。包容度範圍趨於中間，不高也不低時最容易造成極化，而同質性則與極化程度有單調性。極化現象的發生仰賴於人們的同質互動行為以及有限包容性。(三) 從網絡結構來看，網絡的平均路徑長度和機器人的中心性，也就是意見傳播的速度與機器人在網絡中與其他節點的連接度，對於結果產生了顯著影響。當在封閉的網絡或機器人處於邊緣位置時，極化程度反而不明顯。（四）當機器人採取較中間的立場時與當人類對機器人的信心程度降低時，極化趨勢會被削減。此研究為社群媒體機器人活動對公共意見極化和資訊社會現狀的影響提供了解釋，並引入了複雜系統的研究方法，提供未來相關研究更多元的方向。
    The pervasive presence and influence of social bots have become the subject of extensive research in recent years. Studies have revealed that a significant percentage of active accounts are social bots, contributing to the polarization of public sentiment online. Despite numerous studies and models discussing the negative impact of social media bots on online society, there is still a lack of understanding regarding how these bots, when deployed on both ends of the opinion spectrum simultaneously, contribute to confusion and lead to polarized discussions. This study employs an agent-based model in conducting computer simulations of complex social networks, to elucidate how bots, representing diverse ideological perspectives, exacerbate societal divisions. To investigate the dynamics of opinion diffusion and shed light on the phenomenon of polarization caused by the activities of social bots, we introduced bots into a bounded-confidence opinion dynamic model for different social networks, whereby the effects of social bots on other agents were studied to provide a comprehensive understanding of their influence on opinion dynamics.
    The simulations showed that: (1) The symmetrical deployment of bots on both sides of the opinion spectrum intensifies polarization. (2) These effects were observed within specific tolerance and homophily ranges, with low and high user tolerances slowing down polarization, while homophily exhibits a monotonous relationship with the degree of polarization. The occurrence of polarization relies on individuals engaging in homogeneous interactions and exhibiting limited inclusiveness. (3) The average path length of the network and the centrality of the bots, which of them refer to the speed of information spread and the connectivity of bots in network, have a significant impact on the result. (4) Polarization tends to be lower when bots adopt moderate positions and humans exhibit reduced confidence in bots. This research not only offers valuable insights into the implications of social bot activities on the polarization of public opinion and current state of digital society but also leverages the research methods of complex systems, providing recommendation approach for future research.
    Reference: Albert, Réka and Albert-László Barabási, 2002, "Statistical mechanics of complex networks." Reviews of modern physics 74(1): 47.
    Aldayel, Abeer and Walid Magdy, 2022, "Characterizing the role of bots’ in polarized stance on social media." Social Network Analysis and Mining 12(1): 30.
    Averza, Aldo, Khaled Slhoub and Siddhartha Bhattacharyya, 2022, "Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation." IEEE Access 10: 129394-129407.
    Axelrod, Robert, 1997, "The dissemination of culture: A model with local convergence and global polarization." Journal of conflict resolution 41(2): 203-226.
    Axelrod, Robert, Joshua J Daymude and Stephanie Forrest, 2021, "Preventing extreme polarization of political attitudes." Proceedings of the National Academy of Sciences 118(50): e2102139118.
    Azzimonti, Marina and Marcos Fernandes, 2023, "Social media networks, fake news, and polarization." European Journal of Political Economy 76: 102256.
    Babcock, Matthew and Kathleen M Carley, 2022, "Operation gridlock: opposite sides, opposite strategies." Journal of Computational Social Science: 1-25.
    Barabási, Albert-László and Réka Albert, 1999, "Emergence of scaling in random networks." science 286(5439): 509-512.
    Barabási, Albert-László, Réka Albert and Hawoong Jeong, 2000, "Scale-free characteristics of random networks: the topology of the world-wide web." Physica A: statistical mechanics and its applications 281(1-4): 69-77.
    Beatson, Oliver, Rachel Gibson, Marta Cantijoch Cunill and Mark Elliot, 2023, "Automation on twitter: Measuring the effectiveness of approaches to bot detection." Social Science Computer Review 41(1): 181-200.
    Bianchi, Federico and Flaminio Squazzoni, 2015, "Agent‐based models in sociology." Wiley Interdisciplinary Reviews: Computational Statistics 7(4): 284-306.
    Bradshaw, Samantha and Philip N Howard, 2018, "Challenging truth and trust: A global inventory of organized social media manipulation." The computational propaganda project 1: 1-26.
    Bramson, A., Grim, P., Singer, D. J., Fisher, S., Berger, W., Sack, G., & Flocken, C. (2016). Disambiguation of social polarization concepts and measures. The Journal of Mathematical Sociology, 40(2), 80-111.
    Broniatowski, David A, Amelia M Jamison, SiHua Qi, Lulwah AlKulaib, Tao Chen, Adrian Benton, Sandra C Quinn and Mark Dredze, 2018, "Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate." American journal of public health 108(10): 1378-1384.
    Brooks, Brandon, Bernie Hogan, Nicole Ellison, Cliff Lampe and Jessica Vitak, 2014, "Assessing structural correlates to social capital in Facebook ego networks." Social Networks 38: 1-15.
    Bruno, Matteo, Renaud Lambiotte and Fabio Saracco, 2022, "Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election." EPJ Data Science 11(1): 17.
    Cantini, Riccardo, Fabrizio Marozzo, Domenico Talia and Paolo Trunfio, 2022, "Analyzing political polarization on social media by deleting bot spamming." Big Data and Cognitive Computing 6(1): 3.
    Chang, Ho-Chun Herbert, Emily Chen, Meiqing Zhang, Goran Muric and Emilio Ferrara, 2021, "Social bots and social media manipulation in 2020: the year in review." arXiv preprint arXiv:2102.08436.
    Chang, Ho-Chun Herbert and Emilio Ferrara, 2022, "Comparative analysis of social bots and humans during the COVID-19 pandemic." Journal of Computational Social Science 5(2): 1409-1425.
    Cheng, Chun, Yun Luo and Changbin Yu, 2020, "Dynamic mechanism of social bots interfering with public opinion in network." Physica A: statistical mechanics and its applications 551: 124163.
    Deffuant, Guillaume, Frédéric Amblard, Gérard Weisbuch and Thierry Faure, 2002, "How can extremism prevail? A study based on the relative agreement interaction model." Journal of artificial societies and social simulation 5(4).
    Deffuant, Guillaume, David Neau, Frederic Amblard and Gerard Weisbuch, 2001, "Mixing beliefs among interacting agents." Advances in Complex Systems(3): 11.
    DellaPosta, Daniel, Yongren Shi and Michael Macy, 2015, "Why do liberals drink lattes?" American Journal of Sociology 120(5): 1473-1511.
    Erdős, Paul and Alfréd Rényi, 1960, "On the evolution of random graphs." Publ. math. inst. hung. acad. sci 5(1): 17-60.
    Esteban, Joan and Debraj Ray, 2008, "Polarization, fractionalization and conflict." Journal of peace Research 45(2): 163-182.
    Esteban, Joan-Maria and Debraj Ray, 1994, "On the measurement of polarization." Econometrica: Journal of the Econometric Society: 819-851.
    Ferrara, Emilio, 2023, "Social bot detection in the age of ChatGPT: Challenges and opportunities." First Monday.
    Gargiulo, Floriana and Yerali Gandica, 2016, "The role of homophily in the emergence of opinion controversies." arXiv preprint arXiv:1612.05483.
    Gilbert, Nigel, 1999, "Simulation: A new way of doing social science." American Behavioral Scientist 42(10): 1485-1487.
    González-Bailón, Sandra and Manlio De Domenico, 2021, "Bots are less central than verified accounts during contentious political events." Proceedings of the National Academy of Sciences 118(11): e2013443118.
    Granovetter, Mark S, 1973, "The strength of weak ties." American journal of sociology 78(6): 1360-1380.
    Hagen, Loni, Stephen Neely, Thomas E Keller, Ryan Scharf and Fatima Espinoza Vasquez, 2022, "Rise of the machines? Examining the influence of social bots on a political discussion network." Social Science Computer Review 40(2): 264-287.
    Hahn, Ulrike, Jens Ulrik Hansen and Erik J Olsson, 2020, "Truth tracking performance of social networks: How connectivity and clustering can make groups less competent." Synthese 197: 1511-1541.
    Haque, Amanul, Nirav Ajmeri and Munindar P Singh, 2023, "Understanding dynamics of polarization via multiagent social simulation." AI & society: 1-17.
    Hegselmann, Rainer and Ulrich Krause, 2002, "Opinion dynamics and bounded confidence: models, analysis and simulation." Journal of artificial societies and social simulation 5(3).
    Hegselmann, Rainer and Ulrich Krause, 2015, "Opinion dynamics under the influence of radical groups, charismatic leaders, and other constant signals: A simple unifying model." Networks Heterog. Media 10(3): 477-509.
    Hsiao, Yuan, 2022, "Network diffusion of competing behaviors." Journal of Computational Social Science 5(1): 47-68.
    Jacobs, Charity S and Kathleen M Carley, 2022, "Taiwan: China’s Gray Zone Doctrine in Action." Small Wars Journal 3: 1.2-4.3.
    Jacobs, Charity S, Lynnette Hui Xian Ng and Kathleen M Carley, Year, of Conference, "Tracking China’s Cross-Strait Bot Networks Against Taiwan." International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation，: Springer.
    Keijzer, Marijn A and Michael Mäs, 2021, "The strength of weak bots." Online Social Networks and Media 21: 100106.
    Krause, Ulrich, 2000, "A discrete nonlinear and non-autonomous model of consensus formation." Communications in difference equations 2000: 227-236.
    Lee, Ju-Sung, Tatiana Filatova, Arika Ligmann-Zielinska, Behrooz Hassani-Mahmooei, Forrest Stonedahl, Iris Lorscheid, Alexey Voinov, J Gareth Polhill, Zhanli Sun and Dawn C Parker, 2015, "The complexities of agent-based modeling output analysis." Journal of Artificial Societies and Social Simulation 18(4).
    Lönnqvist, Jan-Erik, Juha VA Itkonen, Markku Verkasalo and Panu Poutvaara, 2014, "The Five-Factor Model of personality and Degree and Transitivity of Facebook social networks." Journal of Research in Personality 50: 98-101.
    Lorenz, Jan, 2007, "Continuous opinion dynamics under bounded confidence: A survey." International Journal of Modern Physics C 18(12): 1819-1838.
    Macy, Michael W and Robert Willer, 2002, "From factors to actors: Computational sociology and agent-based modeling." Annual review of sociology 28(1): 143-166.
    Mathias, Jean-Denis, Sylvie Huet and Guillaume Deffuant, 2016, "Bounded confidence model with fixed uncertainties and extremists: The opinions can keep fluctuating indefinitely." Journal of Artificial Societies and Social Simulation 19(1): 6.
    Nazir, Atif, Saqib Raza and Chen-Nee Chuah, Year, of Conference, "Unveiling facebook: a measurement study of social network based applications." Proceedings of the 8th ACM SIGCOMM conference on Internet measurement，.
    Rauchfleisch, Adrian and Jonas Kaiser, 2020, "The false positive problem of automatic bot detection in social science research." PloS one 15(10): e0241045.
    Ravandi, Babak and Fatma Mili, 2019, "Coherence and polarization in complex networks." Journal of Computational Social Science 2: 133-150.
    Robles, José-Manuel, Juan-Antonio Guevara, Belén Casas-Mas and Daniel Gómez, 2022, "When negativity is the fuel. Bots and Political Polarization in the COVID-19 debate." Comunicar 30(71): 63-75.
    Röchert, Daniel, Manuel Cargnino and German Neubaum, 2022, "Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks." Journal of computational social science 5(2): 1159-1205.
    Rolfe, Meredith, 2014, "Social networks and agent‐based modelling." Analytical sociology: 233-260.
    Ross, Björn, Laura Pilz, Benjamin Cabrera, Florian Brachten, German Neubaum and Stefan Stieglitz, 2019, "Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks." European Journal of Information Systems 28(4): 394-412.
    Shen, Puma, 2021, "How China Initiates Information Operations Against Taiwan." Taiwan Strategists 12: 19-34.
    Sohn, Dongyoung, 2022, "Spiral of silence in the social media era: A simulation approach to the interplay between social networks and mass media." Communication Research 49(1): 139-166.
    Stauffer, Dietrich and Hildegard Meyer-Ortmanns, 2004, "Simulation of consensus model of Deffuant et al. on a Barabasi–Albert network." International Journal of Modern Physics C 15(02): 241-246.
    Stella, Massimo, Emilio Ferrara and Manlio De Domenico, 2018, "Bots increase exposure to negative and inflammatory content in online social systems." Proceedings of the National Academy of Sciences 115(49): 12435-12440.
    Strang, David, Robert J David and Saeed Akhlaghpour, 2014, "Coevolution in management fashion: An agent-based model of consultant-driven innovation." American Journal of Sociology 120(1): 226-264.
    Strang, David and Michael W Macy, 2001, "In search of excellence: Fads, success stories, and adaptive emulation." American journal of sociology 107(1): 147-182.
    Stukal, DENIS K and ILYA B Philippov, 2022, "Promoting a Leader or a Cause? An Agent-Based Model of Social Media Bots." Monitoring of Public Opinion: Economic and Social Changes 1: 22-38.
    Törnberg, Petter, 2022, "How digital media drive affective polarization through partisan sorting." Proceedings of the National Academy of Sciences 119(42): e2207159119.
    Uyheng, Joshua and Kathleen M Carley, 2020, "Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines." Journal of computational social science 3: 445-468.
    Uyheng, Joshua, Lynnette Hui Xian Ng and Kathleen M Carley, 2021, "Active, aggressive, but to little avail: characterizing bot activity during the 2020 Singaporean elections." Computational and Mathematical Organization Theory 27(3): 324-342.
    Van Der Walt, Estée and Jan Eloff, 2018, "Using machine learning to detect fake identities: bots vs humans." IEEE access 6: 6540-6549.
    Varol, Onur, Emilio Ferrara, Clayton Davis, Filippo Menczer and Alessandro Flammini, Year, of Conference, "Online human-bot interactions: Detection, estimation, and characterization." Proceedings of the international AAAI conference on web and social media，.
    Waldherr, Annie, Martin Hilbert and Sandra González-Bailón, 2021, "Worlds of Agents: Prospects of Agent-Based Modeling for Communication Research." Communication Methods and Measures 15(4): 243-254.
    Wang, Rui, Dror Walter and Yotam Ophir, 2023, "Not All Bots are Created Equal: The Impact of Bots Classification Techniques on Identification of Discursive Behaviors Around the COVID-19 Vaccine and Climate Change." Social Science Computer Review: 08944393231188472.
    Wattenhofer, Mirjam, Roger Wattenhofer and Zack Zhu, Year, of Conference, "The YouTube social network." Proceedings of the International AAAI Conference on Web and Social Media.
    Watts, Duncan J and Steven H Strogatz, 1998, "Collective dynamics of ‘small-world’networks." nature 393(6684): 440-442.
    Wilensky, Uri and William Rand, 2015, "Collective dynamics of ‘small-world’networks." nature 393(6684): 440-442.
    Wilensky, Uri and William Rand, 2015, An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. Mit Press.
    Yuan, Xiaoyi, Ross J Schuchard and Andrew T Crooks, 2019, "Examining emergent communities and social bots within the polarized online vaccination debate in Twitter." Social media+ society 5(3): 2056305119865465.
    Description: 碩士
    國立政治大學
    社會學系
    111254003
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111254003
    Data Type: thesis
    Appears in Collections:[Department of Sociology] Theses

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