---
language:
- ms
pretty_name: malay-tweets
---

---

## Malaysian Tweets Dataset (from 17 April 2022 until 3 September 2022)

### Original Source
The dataset was originally sourced from [Mesolitica](https://huggingface.co/datasets/mesolitica/snapshot-twitter-2022-09-03). The author split it into 12 files in JSON Lines format, each containing JSON objects separated by newlines. Each file is approximately 3.9 GB.

### Reasons for modifying the original dataset
- To make it more accessible for most users (students, university lecturers, beginner hobbyists, junior data analysts/BI analysts) who are more familiar with CSV files. Many users may not know what JSON Lines format is or how to work with it.
- To reduce analysis time by stripping away less useful attributes and reordering the columns for easier readability.
- To enable processing on normal laptops with limited RAM and CPU by reducing the file size from around 3.9 GB each to 106 MB each. This was achieved by removing less useful attributes and because CSV only writes the column names once, whereas JSON repeats the key for each row.

### Description
This dataset consists of Twitter data collected between April 17, 2022, and September 3, 2022. Due to changes in the Twitter API (following Elon Musk's acquisition of Twitter and its rebranding to X), further snapshots are no longer feasible.

- **Timestamp Range:** 2022-04-17T16:30:07.000Z to 2022-09-03T09:23:52.000Z
- **Total Rows:** 7,075,025
- **Files:** Split across 12 CSV files

### Data Columns
Each CSV file contains the following columns:
- **tweet_created_at:** Time when the tweet was created
- **tweet_timestamp_ms:** Timestamp in milliseconds
- **tweet_id_str:** Unique identifier for the tweet
- **tweet_lang:** Language of the tweet (based on Twitter's tagging, which is often inaccurate. Malay is often tagged as Indonesian (in))
- **tweet_text:** Text content of the tweet
- **tweet_possibly_sensitive:** Indicates if the tweet might contain sensitive content (as determined by Twitter)
- **user_id_str:** Unique identifier for the user
- **user_screen_name:** Account username of the tweet poster
- **user_followers_count:** Number of followers the user has

#### Example Row
```
Mon Aug 01 13:45:07 +0000 2022, 1659361507465, 1.5541009186644582e+18, in, Kedah main macam butoh sekarang, FALSE, 953474382.0, Muddloyy, 1207.0
```

### Language and Region
The tweets are predominantly in Malay and posted by users in Malaysia. The data was filtered based on the following geographic coordinates:
```
locations=[
    99.8568959909,
    0.8232449017,
    119.5213933664,
    7.2037547089,
]
```

### Some rows are messed up so you should remove the rows with empty values first:
```
import pandas as pd
import numpy as np

# Define the data types for specific columns
dtype_spec = {
    'tweet_created_at': str,
    'tweet_timestamp_ms': str,
    'tweet_id_str': str,
    'tweet_lang': str,
    'tweet_text': str,
    'tweet_possibly_sensitive': str,
    'user_id_str': str,
    'user_screen_name': str,
    'user_followers_count': str
}

# Load the CSV file with specified data types
df = pd.read_csv('extracted_data0.csv', dtype=dtype_spec)
print(df.dtypes)

# Replace empty strings with NaN
df.replace('', np.nan, inplace=True)
# Drop rows where any column is empty (NaN)
df = df.dropna()

new_dtypes = {
    'tweet_created_at': str,
    'tweet_timestamp_ms': int,
    'tweet_id_str': str,
    'tweet_lang': str,
    'tweet_text': str,
    'tweet_possibly_sensitive': bool,
    'user_id_str': str,
    'user_screen_name': str,
    'user_followers_count': int
}

df = df.astype(new_dtypes)

# Display the data types to verify
print(df.dtypes)
```