Meta Add library spent plot of Bangladesh

gistfile1.txt

"""
Facebook Ad Library Analysis: BNP vs Jamaat-e-Islami spending strategy.

Reads the Meta Ad Library CSV for Bangladesh (last 7 days ending Feb 4, 2026),
classifies pages by party affiliation, and produces a dot-strip chart
comparing how each party distributes its ad budget.
"""

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from pathlib import Path

# ---------------------------------------------------------------------------
# Config
# ---------------------------------------------------------------------------

DATA_DIR = Path("/your_data_path/FacebookAdLibraryReport_2026-02-04_BD_last_7_days")
CSV_PATH = DATA_DIR / "FacebookAdLibraryReport_2026-02-04_BD_last_7_days_advertisers.csv"
OUTPUT_PNG = DATA_DIR / "ad_strategy.png"
OUTPUT_PDF = DATA_DIR / "ad_strategy.pdf"

COLORS = {
    "bnp_division":   "#3b82f6",
    "bnp_other":      "#93c5fd",
    "jamaat_news":    "#ea580c",
    "jamaat_leader":  "#f59e0b",
    "jamaat_party":   "#fed7aa",
}

# Page IDs verified through ad content review and public reporting (TBS News).

BNP_PAGE_IDS = {
    # Division media cells
    101489482880980,  # Sylhet Division
    108829088795372,  # Barishal Division
    113809844959141,  # Rangpur Division
    110235991986292,  # Chittagong Division
    100998746257599,  # Rajshahi Division
    103192059367184,  # Khulna Division
    114137871592874,  # Mymensingh Division
    105862165769952,  # Comilla Division
    114868274857729,  # Faridpur Division
    110377721977993,  # Dhaka Division
    # Central / official
    266326536843123,  # BNP Media Cell (central)
    353130515348425,  # Bangladesh Nationalist Party-BNP
    48637583555,      # Tarique Rahman
    1067017226489655, # Chattogram 11
    # Campaign pages
    436519556222527,  # "Shobar Age Bangladesh" (BNP slogan)
    632905399898625,  # "Amar Dhan Amar Pran" (BNP paddy symbol)
    # Bengali-only named pages
    103034728693441,  # Dhanmondi Thana BNP
    975452825646784,  # "We are BNP"
    796283883756020,  # Sitakunda Municipality BNP
    640180602509888,  # "Amra BNP Poribar"
}

JAMAAT_PAGE_IDS = {
    # Official
    646141765463753,  # Bangladesh Jamaat-e-Islami
    # Leader / campaign
    1655241494703764, # Dr. Shafiqur Rahman (Ameer)
    246495106022463,  # Dr. Shafiqur Rahman supporter group
    890218124171294,  # "Cholo Eksathe Gori Bangladesh" campaign page
    # News pages (verified via ad content review)
    480337488505055,  # Dhaka News 24 Online
    872833149257837,  # Dhaka 17 News
    # District / local
    659361743938323,  # Meherpur District
    988383787685953,  # Health Division
    550885641431340,  # Fulbaria Upazila
    390203917517769,  # Mirzapur Upazila
    377170452151660,  # Kalmakanda
    100328701372847,  # Bhola-3
    109150173882307,  # Narayanganj City
    560690680469651,  # Mainuddin Ahmad
    242917998894979,  # Dr. Md. Helal Uddin
    138774682912184,  # Nurul Islam Bulbul
    882367374957490,  # "Why Jamaat?"
    107392944247304,  # Chattogram City
    579634365236995,  # Kishoreganj District
    483259565025899,  # Dr. Md. Shafiqul Islam Masud
    103950325070228,  # Charfasson Upazila
    545028055353662,  # Sher-e-Bangla Nagor
    974839482373673,  # Noakhali District
    995392000320962,  # "Adhunik Jamat 24"
    801793316357319,  # Youth & Sports Division
}

JAMAAT_NEWS_PAGE_IDS = {480337488505055, 872833149257837}
JAMAAT_LEADER_PAGE_IDS = {1655241494703764, 246495106022463, 890218124171294}

BNP_DIVISION_PATTERN = r"BNP Media Cell -|BNP Sylhet"


# ---------------------------------------------------------------------------
# Data loading
# ---------------------------------------------------------------------------

def load_ad_data(path: Path) -> pd.DataFrame:
    ads = pd.read_csv(path)
    ads["Amount spent (USD)"] = (
        ads["Amount spent (USD)"]
        .replace("≤100", "50")
        .pipe(pd.to_numeric)
    )
    return ads


# ---------------------------------------------------------------------------
# Classification
# ---------------------------------------------------------------------------

def filter_bnp(ads: pd.DataFrame) -> pd.DataFrame:
    by_id = ads["Page ID"].isin(BNP_PAGE_IDS)
    by_name = ads["Page name"].str.contains("BNP", case=False, na=False)
    by_disclaimer = ads["Disclaimer"].str.contains(
        "BNP|Bangladesh Nationalist Party", case=False, na=False
    )
    is_bnpp = ads["Page name"].str.contains("BNPP", case=False, na=False)
    return ads[(by_id | by_name | by_disclaimer) & ~is_bnpp].copy()


def filter_jamaat(ads: pd.DataFrame) -> pd.DataFrame:
    by_id = ads["Page ID"].isin(JAMAAT_PAGE_IDS)
    by_name = ads["Page name"].str.contains("Jamaat", case=False, na=False)
    by_disclaimer = ads["Disclaimer"].str.contains("Jamaat", case=False, na=False)
    return ads[by_id | by_name | by_disclaimer].copy()


def classify_jamaat_page(page_id: int) -> str:
    if page_id in JAMAAT_NEWS_PAGE_IDS:
        return "news"
    if page_id in JAMAAT_LEADER_PAGE_IDS:
        return "leader"
    return "party"


# ---------------------------------------------------------------------------
# Plotting
# ---------------------------------------------------------------------------

def dot_size(spend: float) -> float:
    return spend * 0.08 + 30


def build_chart(bnp: pd.DataFrame, jamaat: pd.DataFrame) -> plt.Figure:
    plt.rcParams.update({
        "font.size": 10,
        "font.family": "sans-serif",
        "figure.facecolor": "white",
    })

    bnp_spend = bnp["Amount spent (USD)"].sum()
    jamaat_spend = jamaat["Amount spent (USD)"].sum()

    bnp_sorted = bnp.sort_values("Amount spent (USD)", ascending=False).reset_index(drop=True)
    jamaat_sorted = jamaat.sort_values("Amount spent (USD)", ascending=False).reset_index(drop=True)

    fig, ax = plt.subplots(figsize=(11, 5))

    y_bnp, y_jamaat = 1.0, 0.0

    # BNP dots
    bnp_division_ids = set(
        bnp[bnp["Page name"].str.contains(BNP_DIVISION_PATTERN, na=False)]["Page ID"]
    )
    for i, row in bnp_sorted.iterrows():
        color = COLORS["bnp_division"] if row["Page ID"] in bnp_division_ids else COLORS["bnp_other"]
        ax.scatter(
            i, y_bnp,
            s=dot_size(row["Amount spent (USD)"]),
            c=color, edgecolors="white", linewidth=0.4, alpha=0.85, zorder=3,
        )

    # Jamaat dots
    for i, row in jamaat_sorted.iterrows():
        category = classify_jamaat_page(row["Page ID"])
        color = COLORS[f"jamaat_{category}"]
        ax.scatter(
            i, y_jamaat,
            s=dot_size(row["Amount spent (USD)"]),
            c=color, edgecolors="white", linewidth=0.4, alpha=0.85, zorder=3,
        )

    # Label the two dominant Jamaat news pages
    news_spends = jamaat[jamaat["Page ID"].isin(JAMAAT_NEWS_PAGE_IDS)].sort_values(
        "Amount spent (USD)", ascending=False
    )
    news_label = " + ".join(
        f'{row["Page name"]} (${row["Amount spent (USD)"]:,.0f})'
        for _, row in news_spends.iterrows()
    )
    ax.text(
        0.5, y_jamaat - 0.28,
        f"Two largest: {news_label}",
        ha="left", fontsize=7.5, color="#666666",
    )

    # Axes
    ax.set_yticks([y_jamaat, y_bnp])
    ax.set_yticklabels(
        [
            f"Jamaat-e-Islami\n${jamaat_spend:,.0f}  ·  {len(jamaat)} pages",
            f"BNP\n${bnp_spend:,.0f}  ·  {len(bnp)} pages",
        ],
        fontsize=11, fontweight="bold",
    )
    ax.tick_params(axis="y", length=0)
    ax.set_ylim(-0.5, 1.5)
    ax.set_xticks([])
    ax.set_xlabel(
        "Each dot = one Facebook page, sorted by spend (largest to smallest)",
        fontsize=8.5, color="#888888",
    )

    for spine in ax.spines.values():
        spine.set_visible(False)

    # Legend
    legend_items = [
        Line2D([0], [0], marker="o", color="w", markerfacecolor=COLORS["bnp_division"],  markersize=9, label="BNP division cells"),
        Line2D([0], [0], marker="o", color="w", markerfacecolor=COLORS["bnp_other"],     markersize=7, label="BNP other pages"),
        Line2D([0], [0], marker="o", color="w", markerfacecolor=COLORS["jamaat_news"],   markersize=9, label="Jamaat news pages"),
        Line2D([0], [0], marker="o", color="w", markerfacecolor=COLORS["jamaat_leader"], markersize=8, label="Jamaat leader pages"),
        Line2D([0], [0], marker="o", color="w", markerfacecolor=COLORS["jamaat_party"],  markersize=6, label="Jamaat party pages"),
    ]
    ax.legend(
        handles=legend_items, loc="upper right",
        fontsize=8, framealpha=0.95, borderpad=0.8, handletextpad=0.5,
    )

    fig.text(
        0.5, -0.03,
        "Source: Meta Ad Library, Bangladesh, 7 days ending Feb 4, 2026",
        ha="center", fontsize=7.5, color="#aaaaaa", fontstyle="italic",
    )

    plt.tight_layout()
    return fig


# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------

def main():
    ads = load_ad_data(CSV_PATH)

    bnp = filter_bnp(ads)
    jamaat = filter_jamaat(ads)

    bnp_spend = bnp["Amount spent (USD)"].sum()
    jamaat_spend = jamaat["Amount spent (USD)"].sum()
    print(f"BNP: ${bnp_spend:,.0f} ({len(bnp)} pages) | Jamaat: ${jamaat_spend:,.0f} ({len(jamaat)} pages)")

    fig = build_chart(bnp, jamaat)
    fig.savefig(OUTPUT_PNG, dpi=150, bbox_inches="tight", facecolor="white")
    fig.savefig(OUTPUT_PDF, bbox_inches="tight", facecolor="white")
    print(f"Saved: {OUTPUT_PNG.name} and {OUTPUT_PDF.name}")


if __name__ == "__main__":
    main()