{ "format": "dataset-manifest/1", "slug": "community-interactions", "version": 1, "seed": "community-interactions:1", "spec": { "format": "dataset/1", "slug": "community-interactions", "name": "Community Interactions", "tagline": "User interactions including friendships, messages, posts, and comments within a social networking community.", "description": "Users engage in vibrant conversations and share experiences, forming intricate relationships within a social networking platform. The community_interactions table captures the essence of these interactions, detailing how individuals connect through friendships, messages, posts, and comments. Each row represents a unique interaction between users, encompassing the type of interaction, the timestamps, and the participants involved. This structure allows for a deep exploration of user engagement and relationship dynamics within the community.\n\nThe data reveals patterns of connectivity and highlights how certain influencers draw significant attention, which can lead to clustering of interactions around them. By analyzing the interactions found in the table, developers can explore questions about user behavior, engagement rates, and the impact of influential users on community dynamics. This dataset serves as an excellent resource for those looking to practice data analysis skills, especially in understanding network effects and community behavior.\n\nWith the potential to answer questions regarding interaction frequency, popular topics of discussion, and the growth of relationships over time, this dataset is a valuable tool for developers seeking to hone their abilities in social data analysis. The data is synthetic, crafted to simulate real-world interactions, allowing for experimentation without privacy concerns.", "domain": "social", "shape": "graph-edges", "useCases": [ "sql-practice", "teaching" ], "keywords": [ "social networking", "user interactions", "friendships", "community", "data analysis", "engagement" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "community_interactions": { "description": "Each row represents an interaction between two users within the social network, detailing the nature of their relationship. The users are linked through their user IDs, allowing for analysis of interaction patterns.", "rows": 500, "columns": { "id": { "kind": "pk", "description": "Unique identifier for the interaction" }, "user_id_1": { "kind": "uuid", "description": "The ID of the first user involved in the interaction" }, "user_id_2": { "kind": "uuid", "nullable": true, "description": "The ID of the second user involved in the interaction; can be null for solo activities" }, "interaction_type": { "kind": "enum", "values": [ "friend", "message", "post", "comment" ], "description": "The type of interaction that occurred between the users" }, "timestamp": { "kind": "datetime", "from": "2023-01-01", "to": "2023-12-31", "description": "The date and time when the interaction took place", "dayProfile": "waking" } } } }, "structure": "single-table", "traits": [ "categorical", "timestamped" ], "analysisQuestions": [ "What is the most common type of interaction between users?", "How do interaction patterns differ between influencers and regular users?", "Which users are the most connected within the community?", "What time of day sees the highest level of interactions?", "How does the interaction frequency change over time?" ], "suggestedQueries": [ { "title": "Count interactions by type", "sql": "SELECT interaction_type, COUNT(*) FROM community_interactions GROUP BY interaction_type;", "dialect": "postgres" }, { "title": "Find the most active user", "sql": "SELECT user_id_1, COUNT(*) AS interaction_count FROM community_interactions GROUP BY user_id_1 ORDER BY interaction_count DESC LIMIT 1;", "dialect": "postgres" }, { "title": "Interactions over time", "sql": "SELECT DATE(timestamp) AS date, COUNT(*) FROM community_interactions GROUP BY date ORDER BY date;", "dialect": "postgres" }, { "title": "Influencer interactions", "sql": "SELECT user_id_1, COUNT(*) AS interaction_count FROM community_interactions WHERE user_id_1 IN (SELECT DISTINCT user_id_1 FROM community_interactions WHERE interaction_type = 'friend') GROUP BY user_id_1 ORDER BY interaction_count DESC;", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Social Media Analyst", "body": "A social media analyst can utilize this dataset to study user interactions and engagement patterns on a social networking platform. By analyzing the types of interactions and the frequency with which they occur, the analyst can identify key influencers and assess the overall health of the community." }, { "title": "Data Science Educator", "body": "An educator teaching data science can use this dataset in their curriculum to provide students with hands-on experience in SQL querying and data analysis. By encouraging students to explore the dataset, they can learn to derive insights about social behavior and community dynamics." }, { "title": "Community Manager", "body": "A community manager can leverage this dataset to understand how users interact within the platform. By examining the connections and engagement levels between users, they can tailor community-building activities and improve user retention strategies." }, { "title": "Behavioral Researcher", "body": "A behavioral researcher interested in social dynamics can use this dataset to investigate how relationships form and evolve within a community. By analyzing interaction patterns and trends over time, they can derive valuable insights into social behavior and influence." } ] }, "counts": { "community_interactions": 500 }, "stats": { "community_interactions": { "rows": 500, "columns": { "id": { "nulls": 0, "distinct": 500, "min": 1, "max": 500 }, "user_id_1": { "nulls": 0, "distinct": 500 }, "user_id_2": { "nulls": 43, "distinct": 457 }, "interaction_type": { "nulls": 0, "distinct": 4, "min": "comment", "max": "post" }, "timestamp": { "nulls": 0, "distinct": 500, "min": "2023-01-02 13:50:20", "max": "2023-12-31 10:02:48" } } } }, "artifacts": [ { "file": "csv/community_interactions.csv", "format": "csv", "table": "community_interactions", "key": "datasets/community-interactions/v1/csv/community_interactions.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/csv/community_interactions.csv", "bytes": 51410, "sha256": "a141abbf2d4c53278974f8bf09b0376a1c0fa52beabb74837d62dde8aa81d957", "rows": 500, "contentType": "text/csv; charset=utf-8" }, { "file": "community-interactions.json", "format": "json", "table": null, "key": "datasets/community-interactions/v1/community-interactions.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/community-interactions.json", "bytes": 110982, "sha256": "a438535d2a87cabcaa558914c516942accb10469867c152d8e72a5f0c0a4bd8f", "rows": null, "contentType": "application/json; charset=utf-8" }, { "file": "sql/postgres.sql", "format": "sql-postgres", "table": null, "key": "datasets/community-interactions/v1/sql/postgres.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/sql/postgres.sql", "bytes": 61427, "sha256": "b14e4db8057f14d4f1048d130324671767c67a9fe7210e2094b26ac31c744f8b", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mysql.sql", "format": "sql-mysql", "table": null, "key": "datasets/community-interactions/v1/sql/mysql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/sql/mysql.sql", "bytes": 61307, "sha256": "436cff2ed32a51f7091645e533252af764030194fc1737c719c2bc3e5f052631", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mssql.sql", "format": "sql-mssql", "table": null, "key": "datasets/community-interactions/v1/sql/mssql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/sql/mssql.sql", "bytes": 63371, "sha256": "3f0ab75fc47cb6510be63c844cb873d71ee05292c18d3b72400f5edc22617879", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/sqlite.sql", "format": "sql-sqlite", "table": null, "key": "datasets/community-interactions/v1/sql/sqlite.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/sql/sqlite.sql", "bytes": 61272, "sha256": "6dc1466a6a2bbc761373d4043405adb745d12a65cbfac41d47f561d35bd08932", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "prisma/seed.ts", "format": "prisma-seed", "table": null, "key": "datasets/community-interactions/v1/prisma/seed.ts", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/prisma/seed.ts", "bytes": 99209, "sha256": "831f7f27530737b0ad93bb4628da04f74ba9ba2144b546e3751ae8b39e5a2ac1", "rows": null, "contentType": "application/typescript; charset=utf-8" }, { "file": "django/fixtures.json", "format": "django-fixture", "table": null, "key": "datasets/community-interactions/v1/django/fixtures.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/community-interactions/v1/django/fixtures.json", "bytes": 139948, "sha256": "58e98e3a89d04ff9cb3bce4374de7d60f13905f376b3f059c12d2c5b06626fef", "rows": null, "contentType": "application/json; charset=utf-8" } ], "publishedAt": 1788768518792, "updatedAt": 1788768518792 }