How do the strategic political incentives of different regime types influence the structure and sentiment of social media communication by heads of government? Political leaders increasingly view social media as an essential way to connect with their citizens. However, differences in accountability structures and control over information, which vary with the level of democracy, systematically influence the intended audience and purpose of this communication. We analyze the structure and sentiment of over 30,000 French and English language tweets made by 24 political leaders in Sub-Saharan Africa between 2018 and 2021. We find evidence that leaders of more democratic countries tweet more frequently, are less likely to engage directly with specific users, and are more measured in their communication style.
AharonyNoa. 2012. “Twitter Use by Three Political Leaders: an Exploratory Analysis.” Online Information Review36 (4): 587–603.
2.
BarbieriFrancescoEspinosa AnkeLuisCamacho-ColladosJose. 2022. “XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond.” arXiv.
3.
BarbosaLucianoFengJunlan. 2010. “Robust Sentiment Detection on Twitter from Biased and Noisy Data.” In Proceedings of the 23rd International Conference on Computational Linguistics, 36–â44. Association for Computational Linguistics.
4.
BaturoAlexander. 2016. “Cursus Honorum: Personal Background, Careers and Experience of Political Leaders in Democracy and Dictatorship - New Data and Analyses.” Politics and Governance4 (2): 138–57.
5.
BaxterGraemeMarcellaRitaVarfisEvaggelos. 2011. “The Use of the Internet by Political Parties and Candidates in Scotland During the 2010 UK General Election Campaign.” ASLIB Proceedings63 (5): 464–83.
6.
BenderEmily M.GebruTimnitMcMillan-MajorAngelinaShmitchellShmargaret. 2021. “On the Dangers of Stochastic Parrots: Can Language Models be Too Big?” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–23. ACM.
7.
BlaydesLisa. 2011. Elections and Distributive Politics in Mubarak’s Egypt. Cambridge University Press.
8.
Bueno de MesquitaBruceMorrowJamesSiversonRandolphSmithAlastair. 2001. “Political Competition and Economic Growth.” Journal of Democracy12 (1): 58–72.
9.
BulovskyAndrew. 2019. “Authoritarian Communication on Social Media: the Relationship Between Democracy and Leaders’ Digital Communicative Practices.” International Communication Gazette81 (1): 20–45.
10.
ChadwickAndrew. 2013. The Hybrid Media System: Politics and Power. Oxford University Press.
11.
ChoudhuryMonojitDeshpandeAmit. 2021. “How Linguistically Fair are Multilingual Pre-Trained Language Models?” Proceedings of the AAAI Conference on Artificial Intelligence35: 12710–18.
12.
ChunlySereyvicheth. 2020. “Social Media and Counterpublic Spheres in an Authoritarian State: Exploring Online Political Discussions Among Cambodian Facebook Users.” Discourse, Context & Media34: 100382.
13.
CooperAndrew F.WangHongying. 2020. “China’s Engagement with Former Foreign Leaders: in Search of External Validation of Regime Legitimacy.” Asian Survey60 (4): 634–58.
14.
CoppedgeMichaelGerringJohnHenrik KnutsenCarl, et al.2025. V-Dem [Country-Year/Country-Date] Dataset v15. Varieties of Democracy (V-Dem) Project.
15.
CrabtreeCharlesGolderMattGeschwendThomasIndridasonIndridi. 2020. “It Is Not Only what You Say, it Is Also How you Say it: the Strategic Use of Campaign Sentiment.” The Journal of Politics82 (3): 1044–60.
16.
DerczynskiLeonRitterAlanClarkSamBontchevaKalina. 2013. “Twitter Part-Of-Speech Tagging for all: Overcoming Sparse and Noisy Data.” In Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013, 198–206. INCOMA Ltd.
17.
DevlinJacobChangMing-WeiLeeKentonToutanovaKristina. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” arXiv.
18.
DukalskisAlexander. 2021. Making the World Safe for Dictatorship. Oxford University Press.
19.
EllisCali MortensonHorowitzMichael C.StamAllan C.. 2015. “Introducing the LEAD Data Set.” International Interactions41 (4): 718–41.
20.
EnikolopovRubenPetrovaMariaZhuravskayaEkaterina. 2011. “Media and Political Persuasion: Evidence from Russia.” The American Economic Review101 (7): 3253–85.
21.
EvansHeatherCordovaVictoriaSipoleSavannah. 2014. “Twitter Style: an Analysis of How House Candidates Used Twitter in their 2012 Campaigns.” Political Science47 (2): 454–62.
22.
FairRay. 1978. “The Effect of Economic Events on Votes for President.” The Review of Economics and Statistics60 (2): 159–73.
23.
GeddesBarbaraWrightJosephFrantzErica. 2014. “Autocratic Breakdown and Regime Transitions: a New Data Set.” Perspectives on Politics12 (2): 313–31.
24.
GrahamToddBroersmaMarcelHazelhoffKarinvan’t HaarGuido. 2013. “Between Broadcasting Political Messages and Interacting With Voters.” Information, Communication & Society16 (5): 692–719.
25.
GrayThomasGutierrez-MannixCarlos. 2021. “Twitter and the Electoral Connection.” Social Science Quarterly102: 2881–93.
26.
GrimmerJustinMessingSolomonWestwoodSean. 2012. “How Words and Money Cultivate a Personal Vote: the Effect of Legislator Credit Claiming on Constituent Credit Allocation.” American Political Science Review106 (4): 703–19.
27.
GroseChristianMalhotraNeilVan HouwelingRobert Parks. 2014. “Explaining Explanations: How Legislators Explain Their Policy Positions and How Citizens React.” American Journal of Political Science59 (3): 724–43.
28.
GrossJustin H.JohnsonKaylee T.. 2016. “Twitter Taunts and Tirades: Negative Campaigning in the Age of Trump.” Political Science49 (4): 748–54.
29.
GurevitchMichaelColemanStephenBlumlerJay. 2009. “Political Communication - Old and New Media Relationships.” The Annals of the American Academy of Political and Social Science625: 164–81.
30.
GurievSergeiTreismanDaniel. 2019. “Informational Autocrats.” The Journal of Economic Perspectives33 (4): 100–27.
31.
GurievSergeiTreismanDaniel. 2022. Spin Dictators: The Changing Face of Tyranny in the 21st Century. Princeton University Press.
32.
HamanMichael. 2020. “The Use of Twitter by State Leaders and Its Impact on the Public During the COVID-19 Pandemic.” Heliyon6 (11): e05540.
33.
HamanMichaelŠkolníkMilan. 2021. “Politicians on Social Media. the Online Database of Members of National Parliaments on Twitter.” Profesional De La información30 (2): 1–14.
34.
HuttoClayton J.GilbertEric. 2014. “VADER: a Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text.” Proceedings of the International AAAI Conference on Web and Social Media8 (1): 216–25.
35.
JoshiPratikSantySebastinBudhirajiaAmarBaliKalikaChoudhuryMonojit. 2020. “The State and Fate of Linguistic Diversity and Inclusion in the NLP World.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 6282–93. Association for Computational Linguistics.
36.
JungherrAndreas. 2016. “Twitter Use in Election Campaign: a Systematic Literature Review.” Journal of Information Technology & Politics13 (1): 72–91.
KatzJamesBarrisMichaelJainAnshul. 2013. The Social Media President: Barack Obama and the Politics of Digital Engagement. Palgrave-McMillan.
39.
KingGaryPanJenniferRobertsMargaret. 2013. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review107 (2): 326–43.
40.
KrcmaricDanielNelsonStephenRobertsAndrew. 2020. “Studying Leaders and Elites: the Personal Biography Approach.” Annual Review of Political Science23 (1): 133–51.
41.
LevitskyStevenWayLucan. 2002. “The Rise of Competitive Authoritarianism.” Journal of Democracy13 (2): 51–65.
42.
LevySharonJohnNehaLiuLing, et al.2023. “Comparing Biases and the Impact of Multilingual Training Across Multiple Languages.” In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 10260–80. Association for Computational Linguistics.
43.
LiuYinhanOttMyleGoyalNaman, et al.2019. “RoBERTa: a Robustly Optimized BERT Pretraining Approach.” arXiv.
44.
NgangeKingsley. 2020. “Social Media Use in the 2018 Presidential Elections in Cameroon.” In Social Media and Elections in Africa, Vol. 1. Palgrave Macmillan.
45.
NieShangruiFrommMichaelWelchCharles, et al.2024. “Do Multilingual Large Language Models Mitigate Stereotype Bias?” In Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP, 65–83. Association for Computational Linguistics.
46.
NorrisPippa. 2009. “Comparative Political Communications: Common Frameworks or Babelian Confusion?” Government and Opposition44 (3): 321–40.
Rivas-de RocaRubénPérez-CurielConcha. 2023. “Global Political Leaders During the COVID-19 Vaccination: Between Propaganda and Fact-checking.” Politics and the Life Sciences42 (1): 104–19.
49.
RobertsMargaret. 2020. “Resilience to Online Censorship.” American Review of Political Science23: 401–19.
50.
RuijgrokKris. 2021. “Illusion of Control: How Internet Use Generates Anti-Regime Sentiment in Authoritarian Regimes.” Contemporary Politics27 (3): 247–70.
51.
StierSebastianBleierArnimLietzHaikoStrohmaierMarkus. 2018. “Election Campaigning on Social Media: Politicians, Audiences, and the Mediation of Political Communication on Facebook and Twitter.” Political Communication35 (1): 50–74.
52.
TrombleRebekah. 2018. “Thanks for (Actually) Responding! How Citizen Demand Shapes Politicians’ Interactive Practices on Twitter.” New Media & Society20 (2): 676–97.
53.
WeidmannNilsRødEspen. 2019. The Internet and Political Protest in Autocracies. Oxford University Press.
54.
YuRebecca Ping. 2016. “The Relationship Between Passive and Active Non-political Social Media Use and Political Expression on Facebook and Twitter.” Computers in Human Behavior58: 413–20.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
