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It includes various attributes for each profile, such as indicators like the presence of a fake profile picture, username characteristics, profile description length, external URLs, privacy settings, and engagement metrics (e.g., number of posts, followers, and following). metrics like the number of posts, followers, and following such as illustrated in table 1. edge_followed_by DATA 1 #followers DATA 2
username_has_number DATA 1 nums/length fullname DATA 2
full_name_has_number DATA 1 name==username DATA 2
is_private DATA 1 private DATA2
has_channel DATA 1 description length DATA 2
has_guides DATA 1 profile pic DATA 2
edge_follow DATA 1 #follows DATA 2
username_length DATA 1 nums/length username DATA 2
full_name_length DATA 1 fullname words DATA 2
is_joined_recently DATA 1 # posts DATA 2
has_external_url DATA 1 external URL DATA 2
is_business_account DATA 1 FOMULEZ DANS UN SEULE PARAGGAPHE
قال ChatGPT:
The dataset includes various attributes for each profile, providing indicators related to the authenticity and activity of social media accounts. These attributes encompass metrics such as the presence of a fake profile picture, username characteristics, profile description length, external URLs, privacy settings, and engagement metrics. For instance, indicators like the number of posts (#posts), followers (#followers), and following (#following) are included, as illustrated in Table 1. Other key attributes include edge_followed_by (number of followers) and edge_follow (number of follows). The dataset also tracks features like whether the username contains numbers (username_has_number), whether the full name has numbers (full_name_has_number), and if the full name matches the username (name==username). Privacy settings are captured by is_private (indicating if the account is private) and is_business_account (indicating whether the account is a business account). Engagement and content-related metrics include has_channel (whether the profile has a channel), has_guides (whether the profile has guides), description_length (length of the profile description), and is_joined_recently (indicating if the account was recently created). Additionally, the presence of external URLs is noted with has_external_url, while metrics like username_length and full_name_length provide insight into the length and complexity of the username and full name. These attributes form a comprehensive dataset, aiding in the analysis of profile characteristics and engagement patterns.
أنت قلت:
je vous donne les attriuts de deux data set donnez une seule phrase comme ca sans les citer je cite les attriuts dans un tablau 1 It includes various attributes for each profile, such as indicators like the presence of a fake profile picture, username characteristics, profile description length, external URLs, privacy settings, and engagement metrics (e.g., number of posts, followers, and following). metrics like the number of posts, followers, and following such as illustrated in table 1.
قال ChatGPT:
Le jeu de données comprend divers attributs pour chaque profil, tels que des indicateurs de l'authenticité du profil, des caractéristiques du nom d'utilisateur, la longueur de la description du profil, des URL externes, les paramètres de confidentialité et des métriques d'engagement (par exemple, le nombre de publications, de followers et de personnes suivies), comme illustré dans le tableau 1.
أنت قلت:
les 2 dataset
قال ChatGPT:
Les deux jeux de données comprennent divers attributs pour chaque profil, incluant des indicateurs de l'authenticité du profil, des caractéristiques du nom d'utilisateur, la longueur de la description, des URL externes, des paramètres de confidentialité, ainsi que des métriques d'engagement telles que le nombre de publications, de followers et de personnes suivies, comme illustré dans le tableau 1.
أنت قلت:
in english
قال ChatGPT:
The two datasets include various attributes for each profile, such as indicators of profile authenticity, username characteristics, description length, external URLs, privacy settings, and engagement metrics like the number of posts, followers, and following, as illustrated in Table 1.
أنت قلت:
D. Evaluation Method
The dataset is divided into two portions: 70% is allocated for training, enabling the model to learn from data patterns, while the remaining 30% is reserved for testing, used to evaluate the model's accuracy and overall performance.
Accuracy Measures: The study uses four key performance metrics to evaluate the model's effectiveness: recall, precision, F1 score, and accuracy (see table 2):
• True Positives (TP): Correctly identifying fake profiles.
• True Negatives (TN): Accurately recognizing authentic profiles.
• False Negatives (FN): Mistakenly classifying fake profiles as authentic.
• False Positives (FP): Incorrectly classifying authentic profiles as fake.
قال ChatGPT:
D. Evaluation Method
The dataset is divided into two portions: 70% is allocated for training, allowing the model to learn patterns from the data, while the remaining 30% is reserved for testing, enabling the evaluation of the model's accuracy and overall performance. The study employs four key performance metrics to assess the model's effectiveness, as detailed in Table 2:
True Positives (TP): Correctly identifying fake profiles.
True Negatives (TN): Accurately recognizing authentic profiles.
False Negatives (FN): Incorrectly classifying fake profiles as authentic.
False Positives (FP): Incorrectly classifying authentic profiles as fake.
These metrics help measure the model's precision, recall, F1 score, and overall accuracy in distinguishing between fake and authentic profiles.
أنت قلت:
refomulez
قال ChatGPT:
D. Evaluation Method
The dataset is split into two parts: 70% is used for training, allowing the model to learn from data patterns, while the remaining 30% is set aside for testing, to evaluate the model's accuracy and performance. Four key performance metrics are used to assess the model’s effectiveness, as outlined in Table 2:
True Positives (TP): Correctly identifying fake profiles.
True Negatives (TN): Correctly identifying authentic profiles.
False Negatives (FN): Incorrectly classifying fake profiles as authentic.
False Positives (FP): Incorrectly classifying authentic profiles as fake.
These metrics are essential for calculating precision, recall, F1 score, and overall accuracy, allowing a comprehensive evaluation of the model’s performance in detecting fake and real profiles.
يتوفر إصدار جديد من GPT، تابع الدردشة لاستخدام الإصدار القديم أو ابدأ دردشة جديدة للحصول على الإصدار الأحدث.
username_has_number DATA 1 nums/length fullname DATA 2
full_name_has_number DATA 1 name==username DATA 2
is_private DATA 1 private DATA2
has_channel DATA 1 description length DATA 2
has_guides DATA 1 profile pic DATA 2
edge_follow DATA 1 #follows DATA 2
username_length DATA 1 nums/length username DATA 2
full_name_length DATA 1 fullname words DATA 2
is_joined_recently DATA 1 # posts DATA 2
has_external_url DATA 1 external URL DATA 2
is_business_account DATA 1 FOMULEZ DANS UN SEULE PARAGGAPHE
قال ChatGPT:
The dataset includes various attributes for each profile, providing indicators related to the authenticity and activity of social media accounts. These attributes encompass metrics such as the presence of a fake profile picture, username characteristics, profile description length, external URLs, privacy settings, and engagement metrics. For instance, indicators like the number of posts (#posts), followers (#followers), and following (#following) are included, as illustrated in Table 1. Other key attributes include edge_followed_by (number of followers) and edge_follow (number of follows). The dataset also tracks features like whether the username contains numbers (username_has_number), whether the full name has numbers (full_name_has_number), and if the full name matches the username (name==username). Privacy settings are captured by is_private (indicating if the account is private) and is_business_account (indicating whether the account is a business account). Engagement and content-related metrics include has_channel (whether the profile has a channel), has_guides (whether the profile has guides), description_length (length of the profile description), and is_joined_recently (indicating if the account was recently created). Additionally, the presence of external URLs is noted with has_external_url, while metrics like username_length and full_name_length provide insight into the length and complexity of the username and full name. These attributes form a comprehensive dataset, aiding in the analysis of profile characteristics and engagement patterns.
أنت قلت:
je vous donne les attriuts de deux data set donnez une seule phrase comme ca sans les citer je cite les attriuts dans un tablau 1 It includes various attributes for each profile, such as indicators like the presence of a fake profile picture, username characteristics, profile description length, external URLs, privacy settings, and engagement metrics (e.g., number of posts, followers, and following). metrics like the number of posts, followers, and following such as illustrated in table 1.
قال ChatGPT:
Le jeu de données comprend divers attributs pour chaque profil, tels que des indicateurs de l'authenticité du profil, des caractéristiques du nom d'utilisateur, la longueur de la description du profil, des URL externes, les paramètres de confidentialité et des métriques d'engagement (par exemple, le nombre de publications, de followers et de personnes suivies), comme illustré dans le tableau 1.
أنت قلت:
les 2 dataset
قال ChatGPT:
Les deux jeux de données comprennent divers attributs pour chaque profil, incluant des indicateurs de l'authenticité du profil, des caractéristiques du nom d'utilisateur, la longueur de la description, des URL externes, des paramètres de confidentialité, ainsi que des métriques d'engagement telles que le nombre de publications, de followers et de personnes suivies, comme illustré dans le tableau 1.
أنت قلت:
in english
قال ChatGPT:
The two datasets include various attributes for each profile, such as indicators of profile authenticity, username characteristics, description length, external URLs, privacy settings, and engagement metrics like the number of posts, followers, and following, as illustrated in Table 1.
أنت قلت:
D. Evaluation Method
The dataset is divided into two portions: 70% is allocated for training, enabling the model to learn from data patterns, while the remaining 30% is reserved for testing, used to evaluate the model's accuracy and overall performance.
Accuracy Measures: The study uses four key performance metrics to evaluate the model's effectiveness: recall, precision, F1 score, and accuracy (see table 2):
• True Positives (TP): Correctly identifying fake profiles.
• True Negatives (TN): Accurately recognizing authentic profiles.
• False Negatives (FN): Mistakenly classifying fake profiles as authentic.
• False Positives (FP): Incorrectly classifying authentic profiles as fake.
قال ChatGPT:
D. Evaluation Method
The dataset is divided into two portions: 70% is allocated for training, allowing the model to learn patterns from the data, while the remaining 30% is reserved for testing, enabling the evaluation of the model's accuracy and overall performance. The study employs four key performance metrics to assess the model's effectiveness, as detailed in Table 2:
True Positives (TP): Correctly identifying fake profiles.
True Negatives (TN): Accurately recognizing authentic profiles.
False Negatives (FN): Incorrectly classifying fake profiles as authentic.
False Positives (FP): Incorrectly classifying authentic profiles as fake.
These metrics help measure the model's precision, recall, F1 score, and overall accuracy in distinguishing between fake and authentic profiles.
أنت قلت:
refomulez
قال ChatGPT:
D. Evaluation Method
The dataset is split into two parts: 70% is used for training, allowing the model to learn from data patterns, while the remaining 30% is set aside for testing, to evaluate the model's accuracy and performance. Four key performance metrics are used to assess the model’s effectiveness, as outlined in Table 2:
True Positives (TP): Correctly identifying fake profiles.
True Negatives (TN): Correctly identifying authentic profiles.
False Negatives (FN): Incorrectly classifying fake profiles as authentic.
False Positives (FP): Incorrectly classifying authentic profiles as fake.
These metrics are essential for calculating precision, recall, F1 score, and overall accuracy, allowing a comprehensive evaluation of the model’s performance in detecting fake and real profiles.
يتوفر إصدار جديد من GPT، تابع الدردشة لاستخدام الإصدار القديم أو ابدأ دردشة جديدة للحصول على الإصدار الأحدث.