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Анализ негативных комментариев TRUE CRIME

Привет! Я тут активно пытаюсь охватить разные области в сфере Data Science и решила, что было бы классно покопаться c обработкой естественного языка (NLP) на примере комментариев YouTube. Так как после работы я часто смотрю видео Саши Сулим, я задалась вопросом: "Интересно, а есть ли различия в оценке зрителями видео про маньяков в зависимости от пола!? Или нам не важно, кто был убийцей - мужчина/женщина?"

Так я пришла к тому, что могу взять задачку классификации комментариев по оценке их негативности в качестве pet-проекта. То, насколько это получилось, предлагаю оценить вам.

Весь код можно найти в github, а в рамках данной статьи я подробнее опишу процесс исследования данной темы.

Dataset

Для обучения мною был выбран датасет с Kaggle из комментариев, собранных с сайта 2ch.hk и pikabu.ru. Среднестатистический комментарий имеет длину 175 символов, минимальная длина комментария - 21 символ, максимальная - 7 403.

EDA (Exploratory Data Analysis)

Для начала посмотрим что из себя представляет наш датасет. Для этого проведем стандартный анализ:

df = pd.read_csv("./data/labeled.csv", sep=',')
df.shape
>>> (14412, 2)

# преобразуем значения колонки «toxic» к типу (int) для удобства
df["toxic"] = df["toxic"].apply(int)

df["toxic"].value_counts()
>>> 0    9586
>>> 1    4826

# проверим, что нет пустых значений
df[df["toxic"] == 0]["comment"].isna().sum()
>>> 0

Итак, мы выяснили, что датасет представляем собой 14 412 комментариев. Распределение в данном наборе следующее: 4 826 - негативные, 9 586 - нейтральные.

Text preprocessing

Любые сырые данные нужно предобаботать. Для этого есть несколько важных этапов: токенизация, удаление пунктуации и стоп-слов, а также стемминг. Давайте приступим!

# возьмем для примера один комментарий
example = df.iloc[1]["comment"]
print(f"Исходный текст: {example}")
>>> Исходный текст: Хохлы, это отдушина затюканого россиянина, мол, вон, а у хохлов еще хуже. Если бы хохлов не было, кисель их бы придумал.

# разобьем на токены
tokens = word_tokenize(example, language="russian")
print(f"Токены: {tokens}")
>>> Токены: ['Хохлы', ',', 'это', 'отдушина', 'затюканого', 'россиянина', ',', 'мол', ',', 'вон', ',', 'а', 'у', 'хохлов', 'еще', 'хуже', '.', 'Если', 'бы', 'хохлов', 'не', 'было', ',', 'кисель', 'их', 'бы', 'придумал', '.']

# уберем всю пунктуацию и стоп-слова
tokens_without_punct = [i for i in tokens if i not in string.punctuation]
stop_words = stopwords.words("russian")
print(f"Токены без пунктуации: {tokens_without_punct}")
print(f"Токены без пунктуации и стоп слов: {tokens_without_punct_and_stopwords}")
>>> Токены без пунктуации: ['Хохлы', 'это', 'отдушина', 'затюканого', 'россиянина', 'мол', 'вон', 'а', 'у', 'хохлов', 'еще', 'хуже', 'Если', 'бы', 'хохлов', 'не', 'было', 'кисель', 'их', 'бы', 'придумал']
>>> Токены без пунктуации и стоп слов: ['Хохлы', 'это', 'отдушина', 'затюканого', 'россиянина', 'мол', 'вон', 'хохлов', 'хуже', 'Если', 'хохлов', 'кисель', 'придумал']

# далее Стемминг - процесс приведения слов к их базовой/корневой форме. 
tokens_without_punct_and_stopwords = [i for i in tokens_without_punct if i not in stop_words]
snowball = SnowballStemmer(language="russian")
stemmed_tokens = [snowball.stem(i) for i in tokens_without_punct_and_stopwords]
print(f"Токены после стемминга: {stemmed_tokens}")
>>> Токены после стемминга: ['хохл', 'эт', 'отдушин', 'затюкан', 'россиянин', 'мол', 'вон', 'хохл', 'хуж', 'есл', 'хохл', 'кисел', 'придума']

Так как процесс предобработки будет повторяться - создадим для удобства функцию, повторяющую все вышеперечисленные преобразования.

snowball = SnowballStemmer(language="russian")
russian_stop_words = stopwords.words("russian")

def tokenize_sentence(sentence: str, remove_stop_words: bool = True):
    tokens = word_tokenize(sentence, language="russian")
    tokens = [i for i in tokens if i not in string.punctuation]
    if remove_stop_words:
        tokens = [i for i in tokens if i not in russian_stop_words]
    tokens = [snowball.stem(i) for i in tokens]
    return tokens

Отлично, теперь разделим наш датасет на обучающую и тестовую выборку и сравним их распределение.

train_df, test_df = train_test_split(df, test_size = 500, random_state=234)
print(train_df.shape)
print(test_df.shape)
>>> (13912, 2)
>>> (500, 2)

# сравним распределение целевого признака
for sample in [train_df, test_df]:
    print(sample[sample['toxic'] == 1].shape[0] / sample.shape[0])
>>> 0.3356095457159287
>>> 0.314

Получили распределение:

Обучающая выборка	33.56% токсичных комментариев
Тестовая выборка	31.4% токсичных комментариев

Данные равномерно распределены по выборкам, следовательно наша будущая модель должна адекватно оцениваться на тестовых данных.

TF-IDF

Прежде чем приступить к обучению нашей модели мы должны преобразовать наши комментарии в численные массивы. Для этого воспользуемся TF-IDF векторизацией.

TF измеряет насколько часто термин (слово) встречается в документе. Формула для расчета TF:

$\text{TF}(t, d) = \frac{f(t, d)}{N_d}$

где f(t,d) — количество вхождений термина t в документ d , а N_d — общее количество терминов в документе d.

IDF измеряет важность термина по отношению ко всему корпусу документов. Чем реже термин встречается в корпусе, тем выше его IDF. Формула для расчета IDF:

$\text{IDF}(t, D) = \log \left( \frac{N}{\left|\{d \in D : t \in d\}\right|} \right)$

где N — общее количество документов в корпусе D, а ∣{d∈D:t∈d}∣ — количество документов, содержащих термин t.

TF-IDF объединяет TF и IDF для оценки важности термина в конкретном документе. Формула для расчета TF-IDF:

$\text{TF-IDF}(t, d, D) = \text{TF}(t, d) \times \text{IDF}(t, D)$

Для использования TF-IDF применим библиотеку scikit-learn.

# инициализируем векторайзер и применим к нашим выборкам
count_idf_1 = TfidfVectorizer(ngram_range = (1,1), tokenizer=lambda x: tokenize_sentence(x, remove_stop_words=True))
tf_idf_base_1 = count_idf_1.fit(df['comment'])
tf_idf_train_base_1 = count_idf_1.transform(train_df['comment'])
tf_idf_test_base_1 = count_idf_1.transform(test_df['comment'])

# выведем размеры матриц, чтобы убедиться в корректности:
print(tf_idf_train_base_1.shape)
print(tf_idf_test_base_1.shape)
>>> (13912, 36122)
>>> (500, 36122)

Для примера давайте рассмотрим как происходит TF-IDF на одном из комментариев.

sample = test_df.sample(n=1)['comment']
sample_tf_idf = count_idf_1.transform(sample)
sample_tf_idf.shape
>>> (1, 36122)

array = sample_tf_idf.toarray()
array
>>> array([[0., 0., 0., ..., 0., 0., 0.]])

# как выглядит наш комментарий до векторизации
sample
>>> 12391    Что касается 3 млн, у Кия самая дорогая машина...

# извлекаем и выводим ненулевые элементы, которые соответствуют значимым словам:
array[array!= 0]
>>> array([0.27552192, 0.25845753, 0.24785363, 0.19574676, 0.13724815,
           0.25845753, 0.13854953, 0.21636683, 0.18436214, 0.2040751 ,
           0.25845753, 0.23449431, 0.13459448, 0.37887959, 0.20099479,
           0.14063173, 0.15832929, 0.10074052, 0.11669742, 0.25845753,
           0.25845753, 0.06473031])

Теперь, когда наши комментарии имеют векторное представление, мы можем перейти к обучению модели.

Обучение модели

В качестве baseline я использовала логистическую регрессию, т.к она хорошо подходит для задачи бинарной классификации.

Если вы еще не знакомы с данной моделью, но уже слышали про линейную регрессию, то можно сказать, что вы почти знаток. Дело в том, что логистическая регрессия по сути это линейная регрессия, к результату которой в конце применяется логистическая функция (например, сигмоида).

Формула сигмоидной функции:

$\sigma(z) = \frac{1}{1 + e^{-z}}$

где z— линейная комбинация признаков и их весов: z = β₀+β₁x₁+β₂x₂+…+β_nx_n.

Значение σ(z) лежит между 0 и 1, что интерпретируется как вероятность.

# инициализируем модель
model_lr_base_1 = LogisticRegression(solver='lbfgs', random_state=234, max_iter= 10000, n_jobs= -1)

# обучим модель
model_lr_base_1.fit(tf_idf_train_base_1, train_df['toxic'])

# получим прогноз вероятностей классов
predict_lr_base_proba = model_lr_base_1.predict_proba(tf_idf_test_base_1)
predict_lr_base_proba
>>> array([[0.85603587, 0.14396413],
           [0.29448938, 0.70551062],
           [0.41543358, 0.58456642],
           [0.77011541, 0.22988459],
           [0.62820949, 0.37179051],
           ...
           [0.82299013, 0.17700987]])

Каждая строка predict_lr_base_proba представляет собой пару чисел: вероятность не токсичного комментария (первое число) и вероятность токсичного комментария (второе число) соответственно.

Оценка модели

Предлагаю еще сравнить качество нашей модели с случайным классификатором.

def coin_classifier(X:np.array) -> np.array:
    predict = np.random.uniform(0.0, 1.0, X.shape[0])
    return predict
coin_predict = coin_classifier(tf_idf_test_base_1)

Визуализируем ROC-кривые и выведем матрицу ошибок.

# для нашей модели логистической регрессии
fpr_base, tpr_base, _ = roc_curve(test_df['toxic'], predict_lr_base_proba[:, 1])
roc_auc_base = auc(fpr_base, tpr_base)

# для случайного классификатора 
fpr_coin, tpr_coin, _ = roc_curve(test_df['toxic'], coin_predict)
roc_auc_coin = auc(fpr_base, tpr_base)

fig = make_subplots(1,1,
                    subplot_titles = ["Receiver operating characteristic"],
                    x_title="False Positive Rate",
                    y_title = "True Positive Rate"
                   )
fig.add_trace(go.Scatter(
    x = fpr_base,
    y = tpr_base,
    #fill = 'tozeroy',
    name = "ROC base (area = %0.3f)" % roc_auc_base,
    ))
fig.add_trace(go.Scatter(
    x = fpr_coin,
    y = tpr_coin,
    mode = 'lines',
    line = dict(dash = 'dash'),
    name = 'Coin classifier (area = 0.5)'
    ))
fig.update_layout(
    height = 600,
    width = 800,
    xaxis_showgrid=False,
    xaxis_zeroline=False,
    template = 'plotly_dark',
    font_color = 'rgba(212, 210, 210, 1)'
    )

# матрица ошибок
confusion_matrix(test_df['toxic'],
                 (predict_lr_base_proba[:, 1] > 0.5).astype('float'),
                 normalize='true',
                )
>>> array([[0.97959184, 0.02040816],
       [0.35031847, 0.64968153]])

AUC случайного классификатора близок к 0.5, что свидетельствует о том, что этот классификатор неспособен эффективно различать классы.
Модель логистической регрессии показывает значительно лучшие результаты по сравнению с случайным классификатором, что подтверждает ее ценность в задаче классификации комментариев.

Парсинг комментариев

Наконец, перейдем к заключительной части - к нашим комментариям под видео Саши Сулим! Давайте для начала спарсим все комментарии с видео про женщин-маньяков.

# инициализируем Chrome WebDriver с использованием chromedriver-py
driver = webdriver.Chrome(executable_path=binary_path)

# создаем список для результатов парсинга
scrapped = []

# указываем время ожидания в секундах и URL видео
wait = WebDriverWait(driver, 10)
driver.get("https://www.youtube.com/watch?v=Bru4DtUe_CE&t=4s")

# задаем количество прокруток для загрузки комментариев
for item in tqdm(range(200)):
    wait.until(EC.visibility_of_element_located((By.TAG_NAME, "body"))).send_keys(Keys.END)
    time.sleep(2)

# получаем комментарии по тэгу "#content"
for comment in wait.until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, "#content"))):
    scrapped.append(comment.text)

# Закрываем браузер
driver.quit()

Теперь отчистим комментарии от лишнего и сохраним их себе.

comments = []
for part in scrapped[0].split('назад'):
    split_part = part.split('\nОТВЕТИТЬ')[0].split('\n')
    if len(split_part) > 1:
        comments.append(split_part[1])
comments = comments[3:]  # удалим лишние

comments_woman = comments + scrapped[1:]
comments_woman_df = pd.DataFrame({'comment':comments_woman})

comments_woman_df.to_csv('/Users/amakarshina/Desktop/Toxic_comments/Pet-projects/Toxic_comments/data/' + 'comments_woman.csv')
comments_woman_df = comments_woman_df[comments_woman_df['comment'].str.len() > 0]
comments_woman_df

Пример комментариев из видео про убийц-женщин.

Всего под видео о женщинах-убийцах на момент написания этого проекта было 2 358 комментария.

Теперь повторим парсинг для видео про маньяка-мужчину.

driver = webdriver.Chrome(executable_path=binary_path)
scrapped_man = []
wait = WebDriverWait(driver, 10)
driver.get("https://www.youtube.com/watch?v=_8bXHh3pOvA&t=156s")
for item in tqdm(range(200)):
    wait.until(EC.visibility_of_element_located((By.TAG_NAME, "body"))).send_keys(Keys.END)
    time.sleep(2)
for comment in wait.until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, "#content"))):
    scrapped_man.append(comment.text)
driver.quit()
# отчистим от лишнего
comments_man = []
for part in scrapped_man[0].split('назад'):
    split_part = part.split('\nОТВЕТИТЬ')[0].split('\n')
    if len(split_part) > 1:
        comments_man.append(split_part[1])
# сохраним
comments_man = comments_man + scrapped[1:]
comments_man_df = pd.DataFrame({'comment':comments_man})

comments_man_df.to_csv('/Users/amakarshina/Desktop/Toxic_comments/Pet-projects/Toxic_comments/data/' + 'comments_man.csv')
comments_man_df = comments_man_df[comments_man_df['comment'].str.len() > 0]

Пример комментариев из видео про убийцу-мужчину.

Под роликом про Джека-потрошителя на момент написания этого проекта было 2 323 комментария.

Ключевые слова

Для большей наглядности визуализируем ключевые слова, которые чаще всего встречаются в наших комментариях.

man_counter = CountVectorizer(ngram_range=(1, 1))
woman_counter = CountVectorizer(ngram_range=(1, 1))

# применяем счетчики к текстам
man_count = man_counter.fit_transform(comments_man_df['text_clear'])
woman_count = woman_counter.fit_transform(comments_woman_df['text_clear'])

# создаем DataFrame с частотами слов
man_frequence = pd.DataFrame(
    {'word': man_counter.get_feature_names_out(),
     'frequency': man_count.toarray().sum(axis=0)}
).sort_values(by='frequency', ascending=False)

woman_frequence = pd.DataFrame(
    {'word': woman_counter.get_feature_names_out(),
     'frequency': woman_count.toarray().sum(axis=0)}
).sort_values(by='frequency', ascending=False)
display(man_frequence.shape[0])
display(woman_frequence.shape[0])

# фильтруем уникальные слова
man_frequence_filtered = man_frequence.query('word not in @woman_frequence.word')[:100]
woman_frequence_filtered = woman_frequence.query('word not in @man_frequence.word')[:100]

# Создаем облако слов
wordcloud_man = WordCloud(
    background_color="black",
    colormap='Blues',
    max_words=200,
    width=1600,
    height=1600
).generate_from_frequencies(dict(man_frequence_filtered.values))

# создаем облако слов
wordcloud_woman = WordCloud(
    background_color="black",
    colormap='Oranges',
    max_words=200,
    width=1600,
    height=1600
).generate_from_frequencies(dict(woman_frequence.values))

# Визуализируем
fig, ax = plt.subplots(1, 2, figsize=(20, 12))

ax[0].imshow(wordcloud_man, interpolation='bilinear')
ax[1].imshow(wordcloud_woman, interpolation='bilinear')

ax[0].set_title(
    f'Топ 100 слов наиболее частотных,\n уникальных слов в комментариях мужчин',
    fontsize=20
)
ax[1].set_title(
    f'Топ 100 слов наиболее частотных,\n уникальных слов в комментариях женщин',
    fontsize=20
)

ax[0].axis("off")
ax[1].axis("off")

plt.show()

Оценка модели на наших видео

Перейдем к заключительной оценке: найдем доли негативных комментариев при оптимальном пороговом значении.

woman_share_neg = (comments_woman_df['negative_proba'] >  0.575758).sum() / comments_woman_df.shape[0]
woman_share_neg
>>> 0.766156462585034

man_share_neg = (comments_man_df['negative_proba'] >  0.575758).sum() / comments_man_df.shape[0]
man_share_neg
>>> 0.7492447129909365

Выводы

Высокая доля негативных комментариев: Оба видео имеют значительную долю негативно окрашенных комментариев, превышающую 70%. Это указывает на то, что под TRUE CRIME роликами бо́льшая часть комментариев действительно негативная.
Незначительное различие между полами: Доля негативных комментариев под видео про убийц женщин немного превышает долю негативных комментариев под роликом про маньяков мужчин (0.766 против 0.749). Это указывает на то, что в целом различия в тональности комментариев между этими двумя типами видео незначительны.

Надеюсь, что это небольшое исследование было и нтересно для вас, буду рада если подпишитесь на меня тут или на telegram - канал, в котором пишу про свое развитие в области Data Science и делюсь прогрессом. Всем желаю классных проектов!

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19 июл. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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26.06.24 18:46 Jacobethannn098

LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER
26.06.24 18:46 Jacobethannn098

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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18.07.24 20:13 austinagastya

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27.08.24 12:50 James889900

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27.08.24 13:06 James889900

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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19.11.24 03:06 [email protected]

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19.11.24 03:07 [email protected]

My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
21.11.24 04:14 ronaldandre617

Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

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26.11.24 23:12 rickrobinson8

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00:39 [email protected]

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09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.