def plot_cluster(clusters, df):
fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(16, 5))
for i, cluster in enumerate(clusters):
ax = axes[i // 5][i % 5]
years, rates = [], []
for t in [0, 0.2, 0.4]:
ax.axhline(t, linestyle="--", lw=1, color="#999999")
for year, tdf in df.groupby("year"):
years.append(year)
pos = len(tdf[tdf[cluster]])
total = len(tdf)
low, high = pc(pos, total)
ax.plot([year, year], [low, high],
color=color_dict[cluster],
lw=lw,
alpha=alpha)
rates.append(pos / total)
ax.plot(years,
rates,
color=color_dict[cluster],
lw=lw,
alpha=alpha,
marker="o")
ax.set_xlim([2009.5, 2020.5])
if i // 5 == 0:
ax.set_xticks([2010, 2015, 2020])
ax.set_xticklabels([" till ’10", "’15 ", "’20 "])
else:
ax.set_xticks([])
ax.set_xticklabels([])
ax.xaxis.tick_top()
ax.set_xlabel(bebold(cluster))
ax.set_ylim([-0.02, 0.58])
if i % 5 == 0:
ax.set_yticks([0, 0.2, 0.4])
ax.set_yticklabels(["0", ".2", ".4"])
else:
ax.set_yticks([])
ax.set_yticklabels([])
for edge in ["right", "left", "top", "bottom"]:
ax.spines[edge].set_visible(False)
plt.savefig(os.path.join(figs_path, "analyze_clusters.pdf"),
bbox_inches="tight",
pad_inches=0)
"xticks": [0, 0.05, 0.1, 0.15, 0.2, 0.25],
"xticklabels": ["0%", "5%", "10%", "15%", "20%", "25%"]},
"disbelief": {"c": "indianred",
"xlim": [0, 0.25],
"xticks": [0, 0.05, 0.1, 0.15, 0.2],
"xticklabels": ["0%", "5%", "10%", "15%", "20%"]}}
fig_path = os.path.join(sys_path, "results", label + "_platform.pdf")
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(3, 1.2))
for i, (site, gdf) in enumerate(df[df["before_fc"] == True].groupby("site")):
positive_count = gdf[gdf[label] == True]["count"].values[0]
negative_count = gdf[gdf[label] == False]["count"].values[0]
total_count = positive_count + negative_count
postitive_rate = positive_count / total_count
print(site, postitive_rate)
ax.barh(i, postitive_rate, color=plot_args[label]["c"], alpha=0.8)
ci_low, ci_upp = pc(positive_count, total_count, alpha=0.05/3)
ax.plot([ci_low, ci_upp], [i, i], color="k", alpha=0.8)
ax.text(ci_upp, i, " " + str(int(round(postitive_rate * 100))) + "%",
ha="left", va="center", color="gray", alpha=0.8, fontproperties=font)
ax.set_xlim(plot_args[label]["xlim"])
ax.set_xticks(plot_args[label]["xticks"])
ax.set_xticklabels(plot_args[label]["xticklabels"])
ax.set_xlabel(label.capitalize() + " %", fontproperties=font2)
ax.set_ylim([-0.6, 2.6])
ax.set_yticks([0, 1, 2])
ax.set_yticklabels(["Facebook", "Twitter", "YouTube"])
ax.set_ylabel("Platform", fontproperties=font2)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
plt.savefig(fig_path, bbox_inches = "tight", pad_inches = 0)
def plot_event(event_pairs, df):
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(5, 8))
for i, (event_pair, events) in enumerate(event_pairs.items()):
e0, e1 = events[0], events[1]
tdf0, tdf1 = df[df[events[0]]], df[df[events[1]]]
if "election" in event_pair:
tdf0 = tdf0[tdf0["year"] == 2016]
tdf1 = tdf1[tdf1["year"] == 2020]
else:
tdf0 = tdf0[tdf0["year"] < 2013]
tdf1 = tdf1[tdf1["year"] > 2018]
total0 = len(tdf0)
total1 = len(tdf1)
for j, cluster in enumerate(clusters):
pos0 = len(tdf0[tdf0[cluster]])
low0, high0 = pc(pos0, total0)
pos1 = len(tdf1[tdf1[cluster]])
low1, high1 = pc(pos1, total1)
if low0 > high1 or low1 > high0:
ax[i].axhline(j, lw=35, color="#D8D8D8")
d = 0.15
ax[i].plot([low0, high0], [j - d, j - d],
color=color_dict[cluster],
lw=lw,
alpha=alpha)
ax[i].plot([pos0 / total0], [j - d],
color=color_dict[cluster],
marker=markers[i][0],
markersize=int(markers[i][2]))
ax[i].plot([low1, high1], [j + d, j + d],
color=color_dict[cluster],
lw=lw,
alpha=alpha)
ax[i].plot([pos1 / total1], [j + d],
color=color_dict[cluster],
marker=markers[i][1],
markersize=int(markers[i][2]))
ax[i].set_ylim([9.5, -0.5])
ax[i].plot(-1,
-1,
color="k",
label=e0,
marker=markers[i][0],
markersize=int(markers[i][2]))
ax[i].plot(-1,
-1,
color="k",
label=e1,
marker=markers[i][1],
markersize=int(markers[i][2]))
leg = ax[i].legend(bbox_to_anchor=(1, 1.05, 0, 0),
loc="lower right",
borderaxespad=0.,
fontsize=16)
leg.get_frame().set_alpha(0)
ax[0].set_xlim([0, 0.4])
ax[0].set_xticks([0, 0.2, 0.4])
ax[0].set_xticklabels([" 0", ".2 ", ".4 "])
ax[1].set_xlim([0, 0.65])
ax[1].set_xticks([0, 0.3, 0.6])
ax[1].set_xticklabels([" 0", ".3 ", ".6 "])
ax[0].set_yticks([9 - i for i in range(10)])
ax[0].set_yticklabels([bebold(clusters[9 - i]) for i in range(10)])
ax[1].set_yticks([])
ax[1].set_yticklabels([])
for edge in ["right", "left", "top", "bottom"]:
ax[0].spines[edge].set_visible(False)
ax[1].spines[edge].set_visible(False)
plt.savefig(os.path.join(figs_path, "analyze_events.pdf"),
bbox_inches="tight",
pad_inches=0)
control_set = [
"moderated", "meta_like", "meta_dislike", "meta_view",
"linguist_swear", "linguist_laugh", "linguist_emoji", "linguist_fake",
"linguist_administration", "linguist_american", "linguist_nation",
"linguist_personal", "misinfo_factcheck", "misinfo_veracity",
"bias_degree", "bias_party"
]
# plot correlation
df = pd.read_csv(comments_path)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(2.5, 0.6))
w = 0.4
for i, cause in enumerate(cause_set):
control = df[df[cause] == 0]["moderated"].mean()
treated = df[df[cause] == 1]["moderated"].mean()
control_pc = pc(df[df[cause] == 0]["moderated"].sum(),
df[df[cause] == 0]["moderated"].count())
control_err = control - control_pc[0]
treated_pc = pc(df[df[cause] == 1]["moderated"].sum(),
df[df[cause] == 1]["moderated"].count())
treated_err = treated - treated_pc[0]
ax.barh(4 - i - w,
width=control,
height=w,
color="cornflowerblue",
alpha=0.9)
ax.plot([control_pc[0], control_pc[1]], [4 - i - w, 4 - i - w],
color="k",
alpha=0.5)
ax.text(control_pc[1] + 0.001,
4 - i - w,
"{0:.2f}".format(round(control * 100, 2)) + "%",