Multilingual Social Media Reactions
This dataset provides real-time, multilingual social media post analytics from Twitter, TikTok, and Facebook, focusing on Spanish, French, and Arabic content. It includes sentiment scores, engagement metrics, trend indicators, and geo-location data, enabling robust cross-platform sentiment modeling, emerging trend detection, and non-English market analysis for researchers and business analysts.
Sample rows
preview · 8 of 36 rows · all 16 columns| post_idstring | sentiment_labelstring | sentiment_scorefloat | trend_indicatorboolean | platformstring | post_datetimedatetime | author_idstring | author_languagestring | post_languagestring | engagement_likesinteger | engagement_commentsinteger | engagement_sharesinteger | hashtagsstring | geo_countrystring | geo_citystring | post_textstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TW-24A9B1F | positive | 0.78 | true | 2024-06-03T10:14:23Z | tw_user_1021 | es | es | 231 | 34 | 17 | FelizLunes,BuenosDias | ES | Madrid | ¡Gran día para todos! #FelizLunes #BuenosDias | |
| FB-16F4C8A | negative | -0.35 | false | 2024-06-04T19:05:41Z | fb_user_834 | fr | fr | 55 | 9 | 2 | blank | FR | Lyon | Je suis fatigué de la chaleur aujourd'hui. | |
| TT-88D7B3E | positive | 0.61 | true | TikTok | 2024-06-02T15:23:08Z | tt_user_390 | ar | ar | 320 | 47 | 31 | مصر,سعادة | EG | Cairo | يوم جميل في القاهرة! #مصر #سعادة |
| TW-56F2D1C | negative | -0.62 | false | 2024-06-05T08:34:52Z | tw_user_1018 | es | es | 19 | 1 | 0 | blank | MX | Monterrey | No entiendo por qué todo es tan complicado. | |
| FB-23C1E7D | positive | 0.44 | true | 2024-06-03T21:45:19Z | fb_user_842 | fr | fr | 188 | 16 | 12 | Video,Tendance | BE | Bruxelles | Nouvelle vidéo en ligne ! Regardez maintenant. #Video #Tendance | |
| TT-39B2C7A | neutral | 0.15 | true | TikTok | 2024-06-01T17:07:44Z | tt_user_412 | ar | ar | 94 | 11 | 7 | تحدي,ترند | SA | Riyadh | هل شاهدتم آخر تحدي؟ #تحدي #ترند |
| TW-91F3B2A | negative | -0.43 | false | 2024-06-02T09:18:22Z | tw_user_1033 | es | es | 40 | 7 | 1 | blank | AR | Buenos Aires | La situación política me preocupa bastante. | |
| FB-51E7C9F | positive | 0.93 | true | 2024-06-04T14:29:33Z | fb_user_861 | fr | fr | 376 | 22 | 19 | Gratitude | FR | Marseille | Merci à tous pour votre soutien ! #Gratitude | |
| TT-16A3F2B | neutral | -0.18 | false | TikTok | 2024-06-05T12:56:12Z | tt_user_427 | ar | ar | 26 | 3 | 1 | blank | AE | Dubai | الطقس حار جدا اليوم. |
| TW-60B5E2D | positive | 0.85 | true | 2024-06-03T16:10:48Z | tw_user_1045 | es | es | 152 | 18 | 9 | Deporte,Pasión | CO | Bogotá | ¡Viva el fútbol! #Deporte #Pasión | |
| FB-79D4B7A | positive | 0.63 | false | 2024-06-01T11:43:27Z | fb_user_878 | fr | fr | 60 | 6 | 4 | blank | CH | Geneva | La météo est parfaite pour une promenade. | |
| TT-75A2D4B | positive | 0.82 | true | TikTok | 2024-06-04T20:15:50Z | tt_user_435 | ar | ar | 178 | 21 | 10 | موسيقى | MA | Casablanca | أحببت الأغنية الجديدة! #موسيقى |
| TW-38C2E5F | positive | 0.7 | true | 2024-06-05T07:50:13Z | tw_user_1059 | es | es | 86 | 10 | 5 | Comida,México | MX | Guadalajara | La comida mexicana es la mejor. #Comida #México | |
| FB-44C3E2B | negative | -0.27 | false | 2024-06-02T13:27:18Z | fb_user_889 | fr | fr | 33 | 3 | 1 | blank | FR | Paris | Trop de travail cette semaine. | |
| TT-12F8B6D | neutral | 0.37 | true | TikTok | 2024-06-03T18:22:39Z | tt_user_441 | ar | ar | 61 | 8 | 6 | بث_مباشر,ترند | JO | Amman | انضموا إلى البث المباشر الآن! #بث_مباشر #ترند |
| TW-47B3A8C | negative | -0.75 | false | 2024-06-01T20:33:12Z | tw_user_1073 | es | es | 15 | 2 | 0 | blank | CL | Santiago | Hoy no fue mi mejor día. | |
| FB-27B2C8F | positive | 0.58 | true | 2024-06-05T09:56:04Z | fb_user_897 | fr | fr | 115 | 14 | 6 | Mode,Style | FR | Nice | Regardez mon nouveau look! #Mode #Style | |
| TT-22E7C6F | negative | -0.44 | false | TikTok | 2024-06-02T16:45:50Z | tt_user_453 | ar | ar | 22 | 1 | 0 | blank | EG | Alexandria | أحتاج إلى إجازة قريباً. |
| TW-74C2E1A | neutral | 0.34 | true | 2024-06-03T22:12:58Z | tw_user_1088 | es | es | 70 | 8 | 6 | Reto,Viral | PE | Lima | ¡Nuevo reto viral! #Reto #Viral | |
| FB-65D5C9B | neutral | 0.12 | false | 2024-06-04T18:38:15Z | fb_user_901 | fr | fr | 48 | 5 | 2 | blank | FR | Toulouse | Qui veut aller au cinéma ce soir ? |
What the 36 rows show
from the 36-row sampleNegative (sentiment label) stands out: mean sentiment_
- 53%trend_
indicator = true - 0.41median sentiment_
score - 3author languages
- 3post languages
- 65.5median engagement_
likes - 8median engagement_
comments
Median 0.41, from -0.79 to 0.95.
- string 10
- integer 3
- float 1
- datetime 1
- boolean 1
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 10 columns | |||
post_id | string | Unique identifier for the social media post across platformsunique | TW-24A9B1F |
platform | string | Social media platform where the post was published (Twitter, TikTok, Facebook)Twitter · TikTok · Facebook | |
author_id | string | Unique identifier for the post author (platform-specific) | tw_user_1021 |
author_language | string | Primary language of the post author (ISO 639-1 code)es · fr · ar | es |
post_language | string | Language in which the post is written (ISO 639-1 code)es · fr · ar | es |
post_text | string | Full text content of the post | الطقس حار جدا اليوم. |
sentiment_label | string | Categorical sentiment label (positive, neutral, negative)positive · neutral · negative | positive |
hashtags | string | Comma-separated list of hashtags used in the postoptional | FelizLunes,BuenosDias |
geo_country | string | Country code where the post originated (ISO 3166-1 alpha-2)optional | ES |
geo_city | string | City where the post originated, if availableoptional | Madrid |
| Numbers 4 columns | |||
sentiment_score | float | Normalized sentiment score for the post (-1.0 negative, 0 neutral, 1.0 positive)-1 to 1 | 0.78 |
engagement_likes | integer | Number of likes/reactions received by the post0 or more | 231 |
engagement_comments | integer | Number of comments or replies to the post0 or more | 34 |
engagement_shares | integer | Number of times the post was shared or retweeted0 or more | 17 |
| Dates and times 1 column | |||
post_datetime | datetime | Timestamp when the post was published | 2024-06-03T10:14:23Z |
| True or false 1 column | |||
trend_indicator | boolean | Flag indicating if the post is part of an emerging trendoptional | true |
Use it for
A social media dashboard
The trend_
indicator rate, sentiment_ score by sentiment_ label and a breakdown of platform. Excel, Power BI or Tableau. Why do the 10 negative rows have a mean sentiment_
score of -0.54? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Posts36TW-24A9B1F0.78positiveFB-16F4C8A-0.35negativeTT-88D7B3E0.61positive
A software demo
Believable posts with platform, post_
datetime and author_ id to fill a screen in front of a buyer.
This dataset36 rows16 columns
Yours1,000 rows16 columnsgeo_country: UK only
blueprint · multilingual-social-media-reactions
Behind this dataset
Same schema. As many rows as you need.
These 36 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row represents a unique post or comment from Twitter, TikTok, or Facebook.
- Language codes must be one of: es, fr, ar.
- Sentiment scores are calculated using standard NLP models (range: -1 to +1).
- Engagement metrics include likes, shares, and comments, normalized across platforms.
- Timestamp format is ISO 8601; all posts within the past 90 days.
- Content fields exclude personally identifiable information per privacy standards.
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- multilingual-social-media-reactions