{'fraction_non_alphanumeric': 0.029619181946403384, 'fraction_numerical': 0.005641748942172073, 'mean_word_length': 5.120689655172414, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Web 2.0 Mining: Analyzing Social Media

Social media systems such as blogs, photo and link sharing sites, wikis and on-line forums are estimated to produce up to one third of new Web content. One thing that sets these ”Web 2.0” sites apart from traditional Web pages and resources is that they are intertwined with other forms of networked data. Their standard hyperlinks are enriched by social networks, comments, trackbacks, advertisements, tags, RDF data and metadata. We describe recent work on building systems that analyse these emerging social media systems to recognize spam blogs, find opinions on topics, identify communities of interest, derive trust relationships, and detect influential bloggers.