Studying Zelenskiy / Official: War, Peace, and Language
zelenskiy-war-peace.RmdGoal
We study:
- How often the channel mentions war and peace over time.
- Which adjectives are used to describe peace (word before “peace”).
- The dynamics of the word “powerful.”
Setup
library(telegramR)
library(dplyr)
library(stringr)
library(lubridate)
library(tidyr)
library(ggplot2)
# your
api_id <- 123456
api_hash <- "0123456789abcdef0123456789abcdef"
client <- TelegramClient$new("my_session", api_id, api_hash)
client$connect()If not authorized:
client$send_code_request("your_phone_number")After recieving code in Telegram app:
client$sign_in(phone = "your_phone_number", code = "code")Download Messages
You can download a fixed number of recent posts or the full history.
msgs <- download_channel_messages(
client,
"V_Zelenskiy_official",
start_date = "2022-01-01",
limit = Inf
)
head(msgs)
#> # A tibble: 6 × 18
#> message_id channel_id channel_username channel_title date text
#> <dbl> <dbl> <chr> <chr> <dttm> <chr>
#> 1 18225 1463721328 V_Zelenskiy_off… Zelenskiy / … 2026-03-06 11:55:27 "Вдо…
#> 2 18224 1463721328 V_Zelenskiy_off… Zelenskiy / … 2026-03-06 11:06:59 ""
#> 3 18223 1463721328 V_Zelenskiy_off… Zelenskiy / … 2026-03-06 11:06:58 ""
#> 4 18222 1463721328 V_Zelenskiy_off… Zelenskiy / … 2026-03-06 11:06:58 ""
#> 5 18221 1463721328 V_Zelenskiy_off… Zelenskiy / … 2026-03-06 11:06:58 ""
#> 6 18220 1463721328 V_Zelenskiy_off… Zelenskiy / … 2026-03-06 11:06:58 ""
#> # ℹ 12 more variables: views <dbl>, forwards <dbl>, replies <dbl>,
#> # reactions_total <dbl>, reactions_json <chr>, media_type <chr>,
#> # is_forward <lgl>, forward_from_id <dbl>, forward_from_name <chr>,
#> # reply_to_msg_id <dbl>, edit_date <dttm>, post_author <chr>Optional: Reactions and Replies
# Reactions per post
reactions <- download_channel_reactions(
client,
"V_Zelenskiy_official",
limit = 1000
)
# Replies to the latest 50 posts (if comments are enabled)
replies <- download_channel_replies(
client,
"V_Zelenskiy_official",
message_limit = 50,
reply_limit = Inf
)1) War vs. Peace Over Time (weekly dynamics)
# Count mentions per day (simple word match)
war_peace <- msgs %>%
mutate(
war_mentions = str_count(text_lower, "\\bwar\\b") + str_count(text_lower, "\\bвійн.(.||)\\b"),
peace_mentions = str_count(text_lower, "\\bpeace\\b") + str_count(text_lower, "\\bмир(.||)(.||)\\b")
) %>%
mutate(day = lubridate::round_date(day, unit = "week")) %>%
group_by(day) %>%
summarise(
war = sum(war_mentions, na.rm = TRUE),
peace = sum(peace_mentions, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = c(war, peace), names_to = "term", values_to = "count")
# Plot (guard against empty/NA dates)
if (nrow(war_peace) > 0 && all(!is.na(war_peace$day))) {
war_peace %>%
ggplot(aes(day, count, fill = term, group=term)) +
geom_area(position = "fill") +
labs(title = "Mentions of war and peace", x = NULL, y = "Count") +
scale_x_date(expand = c(0, 0, 0, 0)) +
scale_y_continuous(expand = c(0, 0, 0, 0), labels = scales::percent) +
scale_fill_manual(values = c("lightblue", "red")) +
annotate("text", x = ymd("2025-05-01"), y = 0.9, label = "PEACE") +
annotate("text", x = ymd("2025-05-01"), y = 0.1, label = "WAR") +
theme_minimal() +
theme(legend.position = "none")
} else {
tibble::tibble()
}
Example Output (Bundled Sample)
library(dplyr)
library(stringr)
library(lubridate)
library(tidyr)
msgs <- msgs_sample %>%
filter(!is.na(text)) %>%
mutate(
day = as.Date(date),
text_lower = str_to_lower(text)
)
war_peace <- msgs %>%
mutate(
war_mentions = str_count(text_lower, "\\bwar\\b"),
peace_mentions = str_count(text_lower, "\\bpeace\\b")
) %>%
group_by(day) %>%
summarise(
war = sum(war_mentions, na.rm = TRUE),
peace = sum(peace_mentions, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = c(war, peace), names_to = "term", values_to = "count")
if (nrow(war_peace) == 0 || any(is.na(war_peace$day))) {
tibble::tibble()
} else {
war_peace
}
#> # A tibble: 2,004 × 3
#> day term count
#> <date> <chr> <int>
#> 1 2023-06-09 war 0
#> 2 2023-06-09 peace 0
#> 3 2023-06-10 war 0
#> 4 2023-06-10 peace 1
#> 5 2023-06-11 war 0
#> 6 2023-06-11 peace 1
#> 7 2023-06-12 war 0
#> 8 2023-06-12 peace 0
#> 9 2023-06-13 war 2
#> 10 2023-06-13 peace 0
#> # ℹ 1,994 more rows2) Adjectives Before “Peace”
We extract the token immediately preceding “peace”.
# =========================
# Peace/Mир adjective mining + stopword filtering + EN/UKR unification + monthly dynamics
# =========================
# ---- packages ----
library(dplyr)
library(tidyr)
library(stringr)
library(stringi)
library(lubridate)
library(ggplot2)
library(scales)
library(stopwords)
library(RColorBrewer)
# ---- stopwords: EN + UK (ISO) ----
stop_en <- stopwords("en", source = "snowball")
stop_uk <- stopwords("uk", source = "stopwords-iso")
all_stops <- unique(c(stop_en, stop_uk)) %>% str_to_lower()
# ---- ensure we have text_lower + normalized text ----
# If text_lower already exists in msgs, it will be used; otherwise uses `text`.
msgs2 <- msgs %>%
mutate(
text_lower = if ("text_lower" %in% names(.)) text_lower else str_to_lower(text),
text_norm = stringi::stri_trans_nfc(text_lower)
)
# ---- ensure we have a parsed datetime column called `date` ----
# Tries common column names; adjust if needed.
date_col <- intersect(c("date", "created_at", "timestamp", "datetime", "time"), names(msgs2))[1]
if (is.na(date_col)) stop("No date column found. Add/rename a date column (e.g., date, created_at, timestamp).")
msgs2 <- msgs2 %>%
mutate(
date = .data[[date_col]],
date = case_when(
inherits(date, "POSIXt") ~ as.POSIXct(date),
inherits(date, "Date") ~ as.POSIXct(date),
TRUE ~ suppressWarnings(lubridate::ymd_hms(date, quiet = TRUE))
),
date = ifelse(is.na(date), suppressWarnings(lubridate::ymd(.data[[date_col]], quiet = TRUE)), date),
date = as.POSIXct(date, origin = "1970-01-01")
)#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `date = case_when(...)`.
#> Caused by warning:
#> ! Calling `case_when()` with size 1 LHS inputs and size >1 RHS inputs was
#> deprecated in dplyr 1.2.0.
#> ℹ This `case_when()` statement can result in subtle silent bugs and is very inefficient.
#>
#> Please use a series of if statements instead:
#>
#> ```
#> # Previously
#> case_when(scalar_lhs1 ~ rhs1, scalar_lhs2 ~ rhs2, .default = default)
#>
#> # Now
#> if (scalar_lhs1) {
#> rhs1
#> } else if (scalar_lhs2) {
#> rhs2
#> } else {
#> default
#> }
#> ```
# ---- extract the word immediately before peace|мир (row-level, long) ----
adj_long <- msgs2 %>%
mutate(
adj = stringi::stri_extract_all_regex(
text_norm,
# safer than \\b for Unicode: grab letters preceded by non-letter, followed by peace/мир
"(?<!\\p{L})(\\p{L}+(?:[\\-–—]\\p{L}+)*)(?=\\s+[\"'«»„“”’)]*\\s*(?:peace|мир(?:у|ом|і|и|ів|ам|ами|ах)?)\\b)"
)
) %>%
tidyr::unnest_longer(adj) %>%
mutate(
adj = str_to_lower(stringi::stri_trans_nfc(adj))
) %>%
filter(!is.na(adj), adj != "", !(adj %in% all_stops))
# ---- unify EN <-> UKR equivalents (add Ukrainian where missing) ----
# Canonical labels = Ukrainian forms (where possible)
equiv_map <- c(
# =====================
# REAL — реальний
# =====================
"real" = "real",
"реальний"="real","реального"="real","реальному"="real","реальним"="real",
"реальним"="real","реальні"="real","реальна"="real","реальну"="real",
"реальною"="real","реальне"="real","реальних"="real","реальними"="real",
# =====================
# LASTING — тривалий
# =====================
"lasting"="lasting",
"тривалий"="lasting","тривалого"="lasting","тривалому"="lasting",
"тривалим"="lasting","тривалі"="lasting","тривала"="lasting",
"тривалу"="lasting","тривалою"="lasting","тривале"="lasting",
"тривалих"="lasting","тривалими"="lasting",
# =====================
# GLOBAL — глобальний
# =====================
"global"="global",
"глобальний"="global","глобального"="global","глобальному"="global",
"глобальним"="global","глобальні"="global","глобальна"="global",
"глобальну"="global","глобальною"="global","глобальне"="global",
"глобальних"="global","глобальними"="global",
# =====================
# UKRAINIAN — український
# =====================
"ukrainian"="ukrainian",
"український"="ukrainian","українського"="ukrainian","українському"="ukrainian",
"українським"="ukrainian","українські"="ukrainian","українська"="ukrainian",
"українську"="ukrainian","українською"="ukrainian","українське"="ukrainian",
"українських"="ukrainian","українськими"="ukrainian",
# =====================
# JUST — справедливий
# =====================
"just"="just",
"справедливий"="just","справедливого"="just","справедливому"="just",
"справедливим"="just","справедливі"="just","справедлива"="just",
"справедливу"="just","справедливою"="just","справедливе"="just",
"справедливих"="just","справедливими"="just",
# =====================
# TRUE — справжній
# =====================
"true"="true",
"справжній"="true","справжнього"="true","справжньому"="true",
"справжнім"="true","справжні"="true","справжня"="true",
"справжню"="true","справжньою"="true","справжнє"="true",
"справжніх"="true","справжніми"="true",
# =====================
# RELIABLE — надійний
# =====================
"reliable"="reliable",
"надійний"="reliable","надійного"="reliable","надійному"="reliable",
"надійним"="reliable","надійні"="reliable","надійна"="reliable",
"надійну"="reliable","надійною"="reliable","надійне"="reliable",
"надійних"="reliable","надійними"="reliable",
# =====================
# WORTHY — достойний
# =====================
"worthy"="worthy",
"достойний"="worthy","достойного"="worthy","достойному"="worthy",
"достойним"="worthy","достойні"="worthy","достойна"="worthy",
"достойну"="worthy","достойною"="worthy","достойне"="worthy",
"достойних"="worthy","достойними"="worthy"
)
adj_long2 <- adj_long %>%
mutate(
adj_group = recode(adj, !!!equiv_map, .default = adj)
)
# ---- overall counts (merged) ----
peace_adjectives_merged <- adj_long2 %>%
count(adj_group, sort = TRUE)
# show top results
peace_adjectives_merged %>%
filter(n >= 5) %>%
head(20)
#> # A tibble: 20 × 2
#> adj_group n
#> <chr> <int>
#> 1 just 196
#> 2 lasting 167
#> 3 формули 164
#> 4 саміту 148
#> 5 до 140
#> 6 true 77
#> 7 заради 73
#> 8 reliable 61
#> 9 real 56
#> 10 worthy 54
#> 11 саміті 54
#> 12 саміт 50
#> 13 наближення 38
#> 14 досягнення 29
#> 15 наблизити 26
#> 16 для 25
#> 17 формулу 25
#> 18 формула 24
#> 19 global 21
#> 20 в 21
# ---- monthly dynamics (merged) ----
target_groups <- unique(equiv_map)
monthly_adj_merged <- adj_long2 %>%
mutate(month = round_date(date, "month")) %>%
filter(adj_group %in% target_groups) %>%
count(month, adj_group) %>%
arrange(month)
# ---- plot: proportional stacked area (100%) with ColorBrewer ----
# full month sequence
all_months <- seq.Date(
floor_date(min(adj_long2$date, na.rm = TRUE), "month"),
floor_date(max(adj_long2$date, na.rm = TRUE), "month"),
by = "1 month"
)
monthly_adj_merged <- adj_long2 %>%
mutate(month = floor_date(date, "month")) %>%
filter(adj_group %in% target_groups) %>%
count(month, adj_group) %>%
complete(
month = all_months,
adj_group = target_groups,
fill = list(n = 0)
) %>%
arrange(month)
ggplot(monthly_adj_merged, aes(x = month, y = n, color = adj_group, group = adj_group)) +
geom_path() +
scale_y_continuous() +
scale_color_brewer(palette = "Set1") + # You can change palette here
labs(
title = "Proportional Monthly Dynamics (Merged EN/UKR Equivalents)",
x = "Month",
y = "Count",
color = "Adjective (merged)"
) +
theme_minimal()
3) “Powerful” Over Time
powerful <- msgs %>%
mutate(powerful_mentions = str_count(text_lower, "\\bpowerful\\b") + str_count(text_lower, "\\bпотужн.(|.)\\b")) %>%
mutate(day = lubridate::round_date(day, unit = "week")) %>%
group_by(day) %>%
summarise(powerful = sum(powerful_mentions, na.rm = TRUE), .groups = "drop")
powerful %>%
ggplot(aes(day, powerful)) +
geom_line(linewidth = 0.8) +
labs(title = "Usage of the word 'powerful'", x = NULL, y = "Count") +
theme_minimal()