Tarefa crb_papers.q017

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Pergunta feita ao analista

Among activists who go into exile in month index 55, how does their monthly share of tweets referencing foreign-led policy topics change in the month following exile relative to the month prior, compared to activists who remain in-country?

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Trajetória

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1. 0Início

   Among activists who go into exile in month index 55, how does their monthly share of tweets referencing foreign-led policy topics change in the month following exile relative to the month prior, compared to activists who remain in-country?

   ds_0009 (agent)

   | Variable Name | Description | | --- | --- | | actor.id | Stable, URI-style identifier for a public Twitter account belonging to a Venezuelan opposition figure (e.g., legislators, mayors, activists, or journalists). Uniquely identifies the account across all months in the panel. | | month | Month start date (YYYY-MM-01) indicating the calendar month over which tweets were aggregated. Coverage runs from 2013-01-01 through 2020-05-01. | | perc_foreign_policy | Percentage of an account’s tweets in the given month that reference foreign-led policy topics (e.g., diplomacy, sanctions, international pressure, or military action). Constructed via dictionary-based text classification informed by word-embedding similarity trained on the corpus; reported as a percent (0–100), with fractional values possible (e.g., 0.78 means 0.78%). | | tweeted_exile | Indicator (0/1) that the individual associated with the account was residing outside Venezuela during the month. Coded from publicly available reporting and biographical sources; months are flagged once a departure is documented and until return, where known. | | log_tweets | Natural logarithm of the total number of public tweets posted by the account in the month, providing a scale-stable measure of activity volume. | | actor | Text identifier for the account (often the same string as actor.id), included for readability and consistency with external sources. | | months | Sequential month index starting at 1 for the first month in the dataset (January 2013) and increasing by one each calendar month. Useful for aligning time across accounts. | | actorid | Numeric panel identifier for the Twitter account, facilitating joins and fixed account indexing across files or analyses. | Dataset Context: This dataset assembles a monthly panel of Twitter activity from prominent Venezuelan opposition figures across a period of intense political and economic turbulence, spanning January 2013 to May 2020. The roster includes elected officials and well-known public voices who were active on the platform. Researchers identified accounts manually and used Twitter’s full-archive access to retrieve complete public tweet histories. Tweets were then aggregated to the account–month level to create consistent longitudinal records of online discourse. To track whether individuals were inside or outside the country over time, residency status was coded from publicly available sources such as news reports, official statements, and biographical materials. Where exact dates were uncertain, the earliest documented indication of living abroad was used to anchor timing. The monthly topic measure included here—the share of tweets about foreign-led policy actions—was built using dictionaries expanded from seed terms via word-embedding similarity trained on the study corpus, capturing Venezuela-specific vocabulary and multilingual usage. Monthly tweet counts were also recorded and log-transformed to summarize posting volume. The observation window covers major developments in Venezuela’s political landscape, including leadership transitions, contested elections, large protest cycles, and deepening economic and humanitarian crises. During these years, traditional media faced heavy constraints, and social media—particularly Twitter—served as a key venue for public communication, political messaging, and engagement with domestic and international audiences. Many opposition figures maintained active online presences throughout the period, and some relocated abroad; the panel structure allows their communication patterns to be followed continuously month by month. All data derive from public digital traces and public-domain biographical information, and the panel includes only months in which an account posted at least one tweet.

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   ref_first (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"tweeted_exile == 1"}
   4. collapse {"aggs":[{"column":"months","fn":"min","name":"g"}],"by":["actorid"]}

   ref_obs (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"months in [54, 56]"}
   4. collapse {"aggs":[{"column":"perc_foreign_policy","fn":"count","name":"n"},{"column":"tweeted_exile","fn":"max","name":"t"}],"by":["actorid","months"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nboth"},{"column":"t","fn":"max","name":"tmax"}],"by":["actorid"]}

   ref_panel (reference)

   file 3128de21a97807498f279bcb7159973e89621cc6bd4a98c8f319813649f925a8 · frame —

   1. select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}
   2. dropna {"columns":["perc_foreign_policy","log_tweets","months"]}
   3. filter {"query":"months in [54, 56]"}
   4. merge {"how":"left","on":["actorid"],"with":"ref_first"}
   5. merge {"how":"left","on":["actorid"],"with":"ref_obs"}
   6. filter {"query":"nboth == 2 and (g == 55 or ((g != g or g > 56) and tmax == 0))"}
   7. derive {"args":{"cmp":"eq","column":"g","value":55},"name":"treated","op":"indicator"}
   8. derive {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}
   9. derive {"args":{"cmp":"eq","column":"months","value":56},"name":"post","op":"indicator"}
   10. derive {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}
2. 1Transformação
   ref_first · select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}

   22491 → 22491 linhas

   Frame antes → depois: bedb4686cb9f → 8c26e880509f
3. 2Transformação
   ref_first · dropna {"columns":["perc_foreign_policy","log_tweets","months"]}

   22491 → 22491 linhas

   Frame antes → depois: 8c26e880509f → 8c26e880509f
4. 3Transformação
   ref_first · filter {"query":"tweeted_exile == 1"}

   22491 → 2754 linhas

   Frame antes → depois: 8c26e880509f → 4cad94d432f5
5. 4Transformação
   ref_first · collapse {"aggs":[{"column":"months","fn":"min","name":"g"}],"by":["actorid"]}

   2754 → 89 linhas

   Frame antes → depois: 4cad94d432f5 → c717ff2e6bc0
6. 5Transformação
   ref_obs · select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}

   22491 → 22491 linhas

   Frame antes → depois: bedb4686cb9f → 8c26e880509f
7. 6Transformação
   ref_obs · dropna {"columns":["perc_foreign_policy","log_tweets","months"]}

   22491 → 22491 linhas

   Frame antes → depois: 8c26e880509f → 8c26e880509f
8. 7Transformação
   ref_obs · filter {"query":"months in [54, 56]"}

   22491 → 550 linhas

   Frame antes → depois: 8c26e880509f → 424cc15b02f8
9. 8Transformação
   ref_obs · collapse {"aggs":[{"column":"perc_foreign_policy","fn":"count","name":"n"},{"column":"tweeted_exile","fn":"max","name":"t"}],"by":["actorid","months"]}

   550 → 550 linhas

   Frame antes → depois: 424cc15b02f8 → d22c8ce01d2f
10. 9Transformação
    ref_obs · collapse {"aggs":[{"column":"n","fn":"count","name":"nboth"},{"column":"t","fn":"max","name":"tmax"}],"by":["actorid"]}

    550 → 283 linhas

    Frame antes → depois: d22c8ce01d2f → 93aae8f6fde5
11. 10Transformação
    ref_panel · select {"columns":["actorid","months","tweeted_exile","perc_foreign_policy","log_tweets"]}

    22491 → 22491 linhas

    Frame antes → depois: bedb4686cb9f → 8c26e880509f
12. 11Transformação
    ref_panel · dropna {"columns":["perc_foreign_policy","log_tweets","months"]}

    22491 → 22491 linhas

    Frame antes → depois: 8c26e880509f → 8c26e880509f
13. 12Transformação
    ref_panel · filter {"query":"months in [54, 56]"}

    22491 → 550 linhas

    Frame antes → depois: 8c26e880509f → 424cc15b02f8
14. 13Transformação
    ref_panel · merge {"how":"left","on":["actorid"],"with":"ref_first"}

    550 → 550 linhas

    Frame antes → depois: 424cc15b02f8 → 31be9973745f
15. 14Transformação
    ref_panel · merge {"how":"left","on":["actorid"],"with":"ref_obs"}

    550 → 550 linhas

    Frame antes → depois: 31be9973745f → c812303611c6
16. 15Transformação
    ref_panel · filter {"query":"nboth == 2 and (g == 55 or ((g != g or g > 56) and tmax == 0))"}

    550 → 478 linhas

    Frame antes → depois: c812303611c6 → 17c0a2f09839
17. 16Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"g","value":55},"name":"treated","op":"indicator"}

    478 → 478 linhas

    Frame antes → depois: 17c0a2f09839 → d3966b249ed0
18. 17Transformação
    ref_panel · derive {"args":{"column":"treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treated","op":"map"}

    478 → 478 linhas

    Frame antes → depois: d3966b249ed0 → 6e1d5ab6c28a
19. 18Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"months","value":56},"name":"post","op":"indicator"}

    478 → 478 linhas

    Frame antes → depois: 6e1d5ab6c28a → 21f61bbd65f1
20. 19Transformação
    ref_panel · derive {"args":{"columns":["treated","post"]},"name":"treated_post","op":"interaction"}

    478 → 478 linhas

    Frame antes → depois: 21f61bbd65f1 → 26fa36e364ad
21. 20Turno do modelo
    call_499a0288referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "actorid",
        "controls": [
          "treated",
          "post",
          "log_tweets"
        ],
        "dataset": "ref_panel",
        "x": [
          "treated_post"
        ],
        "y": "perc_foreign_policy"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
22. 21Ferramenta
    infer.olsv0.1.0call_63e00d44decidido pelo modeloreexecutável

    Motivo: sm.OLS(y, [const, treated, post, treated_post, log_tweets]).fit(cov_type='cluster', groups=actorid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "actorid",
      "controls": [
        "treated",
        "post",
        "log_tweets"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treated_post"
      ],
      "y": "perc_foreign_policy"
    }

    Figuras call_63e00d44

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treated_post Coefficient on treated_post in the regression of perc_foreign_policy	1,27002	1.2700165764237843	1,159641.1596423291242721	-1,01446 – 3,55449

    Diagnósticos

    • n_obs = 478478
    • k = 55
    • r2 = 0,1636520.16365174894936851
    • df_resid = 473473
    • se_type = "cluster"
    • n_clusters = 239239

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": -0.11133842217858347,
          "ci_low": -1.3246218607318965,
          "coef": -0.71798014145524,
          "p": 0.02056064979616853,
          "se": 0.3079428462567397,
          "t": -2.3315370049435797,
          "term": "Intercept"
        },
        {
          "ci_high": 3.5544905456596854,
          "ci_low": -1.0144573928121168,
          "coef": 1.2700165764237843,
          "p": 0.27454502326297414,
          "se": 1.1596423291242721,
          "t": 1.0951795605657682,
          "term": "treated_post"
        },
        {
          "ci_high": 3.2405930555889997,
          "ci_low": -1.0703995364402195,
          "coef": 1.0850967595743901,
          "p": 0.3223476824341528,
          "se": 1.0941708151593332,
          "t": 0.9917069113348433,
          "term": "treated"
        },
        {
          "ci_high": 2.2893032306691454,
          "ci_low": 1.516652248747072,
          "coef": 1.9029777397081087,
          "p": 5.677653454589945e-19,
          "se": 0.19610614879887608,
          "t": 9.70381475218188,
          "term": "post"
        },
        {
          "ci_high": 0.3608056271710338,
          "ci_low": 0.10786842421936091,
          "coef": 0.23433702569519735,
          "p": 0.0003220661066746909,
          "se": 0.06419786154340898,
          "t": 3.6502310211181186,
          "term": "log_tweets"
        }
      ]
    }

    Digests dos dados

    ref_panel 26fa36e364ad7e9577394db5f95f271f14b64b6d125049c9517a75efd34cd820

    sha256 do resultado

    41c307dc141960a81e063225ab23b59c193dc572048530e315726225192e1c54

    Resultado mostrado ao modelo
23. 22Turno do modelo
    call_c15d2489referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Estimativa (infer.ols): coef.treated_post = 1,270. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_63e00d44:coef.treated_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
24. 23Guarda
    resposta aceita

    Números encontrados

    1,270

    Sem lastro

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    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
25. 24Resposta

    Estimativa (infer.ols): coef.treated_post = 1,270. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_63e00d44:coef.treated_post · Parada: answered

    Figura da resposta: call_63e00d44:coef.treated_post = 1,270021.2700165764237843 (se 1,159641.1596423291242721)
26. 25Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
27. 26Pontuação
    #1figura da resposta próximapassouobrigatória1.270016576 in [-1.002882389, 3.542915542]?
28. 27Pontuação
    #2figura da resposta próximapassouregistrada1.270016576 vs 1.2700165764237865
29. 28Pontuação
    #3figura da resposta próximapassouregistrada1.159642329 vs 1.159642329124281

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• época 1, tentativa 1passoupontuada(exibindo)

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