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Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>It is known that media outlets, such as CNN and FOX, have intrinsic political bias that is reflected in their news reports. The computational prediction of such bias has broad application prospects. However, the prediction is difficult via directly analyzing the news content without high-level context. In contrast, social signals (e.g., the network structure of media followers) provide inspiring cues to uncover such bias. In this article, we realize the first attempt of predicting the latent bias of media outlets by analyzing their social network structures. In particular, we address two key challenges:<jats:italic>network sparsity<\/jats:italic>and<jats:italic>label sparsity<\/jats:italic>. The network sparsity refers to the partial sampling of the entire follower network in practical analysis and computing, whereas the label sparsity refers to the difficulty of annotating sufficient labels to train the prediction model. To cope with the network sparsity, we propose a hybrid sampling strategy to construct a training corpus that contains network information from micro to macro views. Based on this training corpus, a semi-supervised network embedding approach is proposed to learn low-dimensional yet effective network representations. To deal with the label sparsity, we adopt a graph-based label propagation scheme to supplement the missing links and augment label information for model training. The preceding two steps are iteratively optimized to reinforce each other. We further collect a large-scale dataset containing social networks of 10 media outlets together with about 300,000 followers and more than 5 million connections. Over this dataset, we compare our model to a range of state of the art. Superior performance gains demonstrate the merits of the proposed approach. More importantly, the experimental results and analyses confirm the validity of our approach for the computerized prediction of media bias.<\/jats:p>","DOI":"10.1145\/3422181","type":"journal-article","created":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:21:09Z","timestamp":1608682869000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Uncovering Media Bias via Social Network Learning"],"prefix":"10.1145","volume":"12","author":[{"given":"Yiyi","family":"Zhou","sequence":"first","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5110-4526","authenticated-orcid":false,"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinsong","family":"Su","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaquan","family":"Yao","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,12,22]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"585","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","author":"Belkin Mikhail","year":"2001","journal-title":"Proceedings of Neural Information Processing Systems"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","volume-title":"Label propagation and quadratic criterion","author":"Bengio Yoshua","DOI":"10.7551\/mitpress\/6173.003.0016"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2014.2384912"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2015.08.017"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM\u201913)","author":"Cohen Raviv","year":"2013"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM\u201911)","author":"Conover Michael","year":"2011"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-2508.2005.00349.x"},{"key":"e_1_2_1_10_1","volume-title":"Blue State, Rich State","author":"Gelman Andrew"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.socnet.2013.07.004"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1162\/003355305775097542"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS\u201910)","volume":"1","author":"Gutmann Michael","year":"2010"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 31st International Conference on Machine Learning (ICML\u201914)","volume":"14","author":"Quoc"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104409"},{"key":"e_1_2_1_17_1","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. 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