{'fraction_non_alphanumeric': 0.013015184381778741, 'fraction_numerical': 0.0, 'mean_word_length': 5.817733990147783, '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}}
Discovering Industrial and Geographical Separation in Online Professional Networks: Information Cocoons-Like Effect on Professional Information Exchange

Information cocoons, a phenomenon of biased information obtaining and view reinforcement guided by user preferences, have been found in political communications in online social networks, but their existence in other platforms has not been proved. In the study, we identify and measure information cocoons in online professional networks to learn the industrial and geographical separation on career communication. Using the dataset of one million users in LinkedIn, we construct the network of professional information exchange by co-visit relations of profile pages. Evidences of information cocoons are found in the topological structure of the network. Furthermore, we gauge information cocoons by two main indicators of homophily and polarization. We define assortativity coefficient to quantify homophily and design a leaning parameter to test polarization in industry and locality. Node centrality is also used to explore the key factors affecting professional information exchange. Our results confirm the existence of information cocoons and the contribution of geographical relation to this effect in OPNs. The proposed method can be exploited to recognize and assess the presence of information cocoons quantitatively.