Communicative Roles and Structural Fragmentation in LPDP Scholarship Discourse on Platform X
DOI:
10.70211/ltsm.3026-7196.531Published:
2026-09-15Downloads
Abstract
Public discussion of scholarship policy involves different communicative activities, including seeking information, sharing experience, and questioning institutional accountability. Understanding how these activities connect requires attention to both network structure and the roles of technologically mediated accounts. This study examines LPDP scholarship discourse on Platform X from 1 January to 31 March 2026 using quantitative social network analysis. A corpus of 823 interaction-bearing public posts generated a directed network of 489 accounts and 386 unique edges. Five communities were reported, with modularity Q = 0.642. Centrality measures differentiated interaction destinations from initiating accounts and potential brokers: @grok had the highest in-degree (19) and a reported betweenness score of 0.00, whereas @tanyakanrl had the highest out-degree (16) and betweenness score (36.00). Interpreted through communication affordances and human–machine communication, these patterns indicate that receiving attention and occupying connecting positions are distinct communicative roles. The findings support structural fragmentation within the observed network, while thematic labels remain descriptive and do not establish ideological agreement or an echo chamber. The study contributes a role-sensitive interpretation of digital public discourse that separates observable interaction from message meaning and communicative influence. For institutional communication, it identifies potential points for examining how explanations enter conversations organized around different information needs.
Keywords:
Digital Public Discourse Communication Affordances Human–Machine Communication Structural Fragmentation Social Network AnalysisReferences
[1] S. K. Evans, K. E. Pearce, J. Vitak, and J. W. Treem, “Explicating affordances: A conceptual framework for understanding affordances in communication research,” Journal of Computer-Mediated Communication, vol. 22, no. 1, pp. 35–52, 2017. https://doi.org/10.1111/jcc4.12180
[2] J. J. P. Latupeirissa and N. M. W. Cistadewi, “A systematic review on social media echo chambers and political persuasion,” Jurnal Studi Komunikasi, vol. 9, no. 2, pp. 562–576, 2025. https://doi.org/10.25139/jsk.v9i2.10041
[3] S. Sumartias, D. A. T. Pulubuhu, Sudarmono, A. N. Adi, and E. Ratnasari, “Democracy in the Indonesian digital public sphere: Social network analysis of Twitter users’ responses to the issue of Nationalism Knowledge Test at the Corruption Eradication Commission (TWK-KPK),” Jurnal Ilmu Sosial dan Ilmu Politik, vol. 26, no. 3, pp. 240–257, 2023. https://doi.org/10.22146/jsp.70896
[4] P. N. Aisyah, G. N. Bakry, and N. A. Sjafirah, “Analisis jejaring sosial peran pers dalam penyebaran informasi terkait kebijakan PPKM,” Jurnal Komunikasi Global, vol. 11, no. 1, pp. 43–65, 2022. https://doi.org/10.24815/jkg.v11i1.24555
[5] M. A. Akbar, M. A. B. Amril, R. Syahira, F. R. Latisha, and N. Jihan, “Analisis struktur jaringan komunikasi #SEAGAMES2022 di Twitter menggunakan pendekatan Social Network Analysis (SNA),” Jurnal Studi Komunikasi dan Media, vol. 26, no. 1, pp. 1–16, 2022. https://doi.org/10.17933/jskm.2022.4780
[6] A. S. B. Dahana, “Social Network Analysis percakapan pengguna X seputar isu pengungsi Rohingya di Indonesia,” Jurnal Pustaka Komunikasi, vol. 8, no. 1, pp. 196–210, 2025. https://doi.org/10.32509/pustakom.v8i1.5000
[7] A. O. L. Hamanduna and P. Widjanarko, “Discourse network on the revision of Indonesian information and electronic transaction law,” Jurnal Studi Komunikasi, vol. 7, no. 2, pp. 519–538, 2023. https://doi.org/10.25139/jsk.v7i2.5496
[8] M. I. Khatami, “Discourse network analysis (DNA): Aktivisme digital dalam perdebatan isu ‘Presiden Tiga Periode’ di Twitter,” Jurnal Audience, vol. 5, no. 1, pp. 80–94, 2022. https://doi.org/10.33633/ja.v5i1.5484
[9] R. N. Priyansyah, R. R. Sulaiman, and F. R. Firdaus, “When the opposition dominates the conversation (Twitter network analysis about the controversial Indonesian Constitutional Court decision),” Jurnal Komunikasi Indonesia, vol. 13, no. 1, 2024. https://doi.org/10.7454/jkmi.v13i1.1221
[10] A. P. Bate and H. F. Amrullah, “Hashtag activism and football tragedy commemoration: Social network analysis in the #100harikanjuruhan hashtag on Twitter,” Jurnal Komunikasi Indonesia, vol. 13, no. 1, 2024. https://doi.org/10.7454/jkmi.v13i1.1226
[11] E. Prihantoro and R. W. Ramadhani, “Social Network Analysis: #BlackLivesMatter distribution at actor level and system level,” Jurnal Komunikasi Ikatan Sarjana Komunikasi Indonesia, vol. 6, no. 2, pp. 275–283, 2021. https://doi.org/10.25008/jkiski.v6i2.577
[12] L. Nurhajati, X. A. Wijayanto, L. R. Fitriyani, and D. Rachmawati, “Building democracy and freedom of expression by fighting the COVID-19 infodemic in the digital space among the younger generation in Indonesia,” Jurnal Komunikasi Ikatan Sarjana Komunikasi Indonesia, vol. 8, no. 1, pp. 239–249, 2023. https://doi.org/10.25008/jkiski.v8i1.861
[13] C. N. Zempi and R. Rahayu, “Social media in the anticorruption movement: Social network analysis on the refusal of the ‘Koruptor Boleh Nyaleg’ decision on Twitter,” Jurnal Komunikasi Indonesia, vol. 8, no. 2, 2019. https://doi.org/10.7454/jki.v8i2.11195
[14] A. L. Guzman and S. C. Lewis, “Artificial intelligence and communication: A Human–Machine Communication research agenda,” New Media & Society, vol. 22, no. 1, pp. 70–86, 2020. https://doi.org/10.1177/1461444819858691
[15] M. Cinelli, G. De Francisci Morales, A. Galeazzi, W. Quattrociocchi, and M. Starnini, “The echo chamber effect on social media,” Proceedings of the National Academy of Sciences, vol. 118, no. 9, Art. no. e2023301118, 2021. https://doi.org/10.1073/pnas.2023301118
[16] M. Del Vicario, A. Bessi, F. Zollo, F. Petroni, A. Scala, G. Caldarelli, H. E. Stanley, and W. Quattrociocchi, “The spreading of misinformation online,” Proceedings of the National Academy of Sciences, vol. 113, no. 3, pp. 554–559, 2016. https://doi.org/10.1073/pnas.1517441113
[17] I. Himelboim, S. McCreery, and M. Smith, “Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter,” Journal of Computer-Mediated Communication, vol. 18, no. 2, pp. 40–60, 2013. https://doi.org/10.1111/jcc4.12001
[18] I. Himelboim, K. D. Sweetser, S. F. Tinkham, K. Cameron, M. Danelo, and K. West, “Valence-based homophily on Twitter: Network analysis of emotions and political talk in the 2012 presidential election,” New Media & Society, vol. 18, no. 7, pp. 1382–1400, 2016. https://doi.org/10.1177/1461444814555096
[19] I. Himelboim, M. A. Smith, L. Rainie, B. Shneiderman, and C. Espina, “Classifying Twitter topic-networks using social network analysis,” Social Media + Society, vol. 3, no. 1, 2017. https://doi.org/10.1177/2056305117691545
[20] J. K. Lee, J. Choi, C. Kim, and Y. Kim, “Social media, network heterogeneity, and opinion polarization,” Journal of Communication, vol. 64, no. 4, pp. 702–722, 2014. https://doi.org/10.1111/jcom.12077
[21] S. Messing and S. J. Westwood, “Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online,” Communication Research, vol. 41, no. 8, pp. 1042–1063, 2014. https://doi.org/10.1177/0093650212466406
[22] R. K. Garrett and N. J. Stroud, “Partisan paths to exposure diversity: Differences in pro- and counterattitudinal news consumption,” Journal of Communication, vol. 64, no. 4, pp. 680–701, 2014. https://doi.org/10.1111/jcom.12105
[23] B. Enjolras and A. Salway, “Homophily and polarization on political Twitter during the 2017 Norwegian election,” Social Network Analysis and Mining, vol. 13, Art. no. 10, 2023. https://doi.org/10.1007/s13278-022-01018-z
[24] P. Darius, “Who polarizes Twitter? Ideological polarization, partisan groups and strategic networked campaigning on Twitter during the 2017 and 2021 German Federal elections ‘Bundestagswahlen’,” Social Network Analysis and Mining, vol. 12, Art. no. 151, 2022. https://doi.org/10.1007/s13278-022-00958-w
[25] M. Osterbur and C. Kiel, “Tweeting in echo chambers? Analyzing Twitter discourse between American Jewish interest groups,” Journal of Information Technology & Politics, vol. 18, no. 2, pp. 194–213, 2021. https://doi.org/10.1080/19331681.2020.1838396
[26] L. Iandoli, S. Primario, and G. Zollo, “The impact of group polarization on the quality of online debate in social media: A systematic literature review,” Technological Forecasting and Social Change, vol. 170, Art. no. 120924, 2021. https://doi.org/10.1016/j.techfore.2021.120924
[27] M. Zappavigna, “Ambient affiliation: A linguistic perspective on Twitter,” New Media & Society, vol. 13, no. 5, pp. 788–806, 2011. https://doi.org/10.1177/1461444810385097
[28] J. T. Hancock, M. Naaman, and K. Levy, “AI-mediated communication: Definition, research agenda, and ethical considerations,” Journal of Computer-Mediated Communication, vol. 25, no. 1, pp. 89–100, 2020. https://doi.org/10.1093/jcmc/zmz022
[29] S. S. Sundar, “Rise of machine agency: A framework for studying the psychology of Human–AI Interaction (HAII),” Journal of Computer-Mediated Communication, vol. 25, no. 1, pp. 74–88, 2020. https://doi.org/10.1093/jcmc/zmz026
[30] M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences, vol. 99, no. 12, pp. 7821–7826, 2002. https://doi.org/10.1073/pnas.122653799
[31] L. Peel, D. B. Larremore, and A. Clauset, “The ground truth about metadata and community detection in networks,” Science Advances, vol. 3, no. 5, Art. no. e1602548, 2017. https://doi.org/10.1126/sciadv.1602548
[32] M. T. Schaub, J.-C. Delvenne, M. Rosvall, and R. Lambiotte, “The many facets of community detection in complex networks,” Applied Network Science, vol. 2, Art. no. 4, 2017. https://doi.org/10.1007/s41109-017-0023-6
[33] U. Brandes, “A faster algorithm for betweenness centrality,” Journal of Mathematical Sociology, vol. 25, no. 2, pp. 163–177, 2001. https://doi.org/10.1080/0022250X.2001.9990249
[34] F. Pierri, C. Piccardi, and S. Ceri, “Topology comparison of Twitter diffusion networks effectively reveals misleading information,” Scientific Reports, vol. 10, Art. no. 1372, 2020. https://doi.org/10.1038/s41598-020-58166-5
[35] V. A. Traag, L. Waltman, and N. J. van Eck, “From Louvain to Leiden: Guaranteeing well-connected communities,” Scientific Reports, vol. 9, Art. no. 5233, 2019. https://doi.org/10.1038/s41598-019-41695-z
[36] S. Fortunato and M. Barthélemy, “Resolution limit in community detection,” Proceedings of the National Academy of Sciences, vol. 104, no. 1, pp. 36–41, 2007. https://doi.org/10.1073/pnas.0605965104
[37] C. A. Bail, L. P. Argyle, T. W. Brown, J. P. Bumpus, H. Chen, M. B. F. Hunzaker, J. Lee, M. Mann, F. Merhout, and A. Volfovsky, “Exposure to opposing views on social media can increase political polarization,” Proceedings of the National Academy of Sciences, vol. 115, no. 37, pp. 9216–9221, 2018. https://doi.org/10.1073/pnas.1804840115
[38] J. Brundidge, “Encountering ‘difference’ in the contemporary public sphere: The contribution of the Internet to the heterogeneity of political discussion networks,” Journal of Communication, vol. 60, no. 4, pp. 680–700, 2010. https://doi.org/10.1111/j.1460-2466.2010.01509.x
[39] G. A. Huber and N. Malhotra, “Political homophily in social relationships: Evidence from online dating behavior,” Journal of Politics, vol. 79, no. 1, pp. 269–283, 2017. https://doi.org/10.1086/687533
[40] L. M. Aiello, A. Barrat, R. Schifanella, C. Cattuto, B. Markines, and F. Menczer, “Friendship prediction and homophily in social media,” ACM Transactions on the Web, vol. 6, no. 2, Art. no. 9, 2012. https://doi.org/10.1145/2180861.2180866
[41] F. Zollo, P. K. Novak, M. Del Vicario, A. Bessi, I. Mozetič, A. Scala, G. Caldarelli, and W. Quattrociocchi, “Emotional dynamics in the age of misinformation,” PLoS ONE, vol. 10, no. 9, Art. no. e0138740, 2015. https://doi.org/10.1371/journal.pone.0138740
[42] E. Bakshy, S. Messing, and L. A. Adamic, “Exposure to ideologically diverse news and opinion on Facebook,” Science, vol. 348, no. 6239, pp. 1130–1132, 2015. https://doi.org/10.1126/science.aaa1160
[43] S. Flaxman, S. Goel, and J. M. Rao, “Filter bubbles, echo chambers, and online news consumption,” Public Opinion Quarterly, vol. 80, no. S1, pp. 298–320, 2016. https://doi.org/10.1093/poq/nfw006
[44] E. Dubois and G. Blank, “The echo chamber is overstated: The moderating effect of political interest and diverse media,” Information, Communication & Society, vol. 21, no. 5, pp. 729–745, 2018. https://doi.org/10.1080/1369118X.2018.1428656
[45] P. Barberá, J. T. Jost, J. Nagler, J. A. Tucker, and R. Bonneau, “Tweeting from left to right: Is online political communication more than an echo chamber?” Psychological Science, vol. 26, no. 10, pp. 1531–1542, 2015. https://doi.org/10.1177/0956797615594620
[46] M. B. Petersen, A. Osmundsen, and K. Arceneaux, “The ‘need for chaos’ and motivations to share hostile political rumors,” American Political Science Review, vol. 117, no. 4, pp. 1486–1505, 2023. https://doi.org/10.1017/S0003055422001447
[47] R. K. Garrett, “Echo chambers online? Politically motivated selective exposure among Internet news users,” Journal of Computer-Mediated Communication, vol. 14, no. 2, pp. 265–285, 2009. https://doi.org/10.1111/j.1083-6101.2009.01440.x
License
Copyright (c) 2026 Fathiyyah Azizah, Nadya Tristina, Elisabeth Satya Wacana Patricia S, Reni Nuraeni, Ai Indah Cahyani
This work is licensed under a Creative Commons Attribution 4.0 International License.
