{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Data Science Tech Brief By HackerNoon","title":"Building an Auditable Pipeline for Cross-Language News Coverage Analysis","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/6733bad1\"></iframe>","width":"100%","height":180,"duration":1711,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/building-an-auditable-pipeline-for-cross-language-news-coverage-analysis.\nHow I turned 2,565 Arabic news reports on Lebanon’s war into 234 auditable incidents with Python, Media Cloud, and a traceable data pipeline.\nCheck more stories related to data-science at: https://hackernoon.com/c/data-science.\n            You can also check exclusive content about #data-engineering, #data-pipeline, #media-analysis, #data-provenance, #cross-language-information, #python-data-pipelines, #temporal-data-normalization, #data-reconciliation,  and more.\nThis story was written by: @soufan. Learn more about this writer by checking @soufan's about page,\n            and for more stories, please visit hackernoon.com.\nThe project aimed to connect international news coverage to specific fatal strikes in a small Lebanese town by reconstructing what had happened accurately enough to search for it in international news databases. The pipeline was built to turn a pile of Arabic news bulletins into a clean, traceable chain of incidents, locations, and dates.","thumbnail_url":"https://img.transistorcdn.com/8VxAgS1Ll3FJEERcAdhFdqqXJMnE7OfD2RUvrjauLt0/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjY4LzE2ODM1/ODI1ODUtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}