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We propose a method to rate the credibility of news articles using three clues: (1) commonality of the contents of articles among different news publishers; (2) numerical agreement versus contradiction of numerical values reported in the articles; and (3) objectivity based on subjective speculative phrases and news sources. We tested this method on news stories taken from seven different news sites on the Web. The average agreement between the system-produced "credibility" and the manual judgments of three human assessors on the 52 sample articles was 69.1%. The limitations of the current approach and future directions are discussed.

Abstract. In this paper we introduce the NTCIR6 Opinion Analysis Pilot Task, information about the Chinese, Japanese, and English data, plans for future opinion analysis tasks at NTCIR, and a brief overview of the evaluation results. This pilot task is a sentence-level opinion identification and polarity detection task run over data from a comparable corpus in three languages: Chinese, English, and Japanese. We have manually annotated documents for this task in each language, producing what we believe to be the first multilingual opinion analysis data set over comparable data. Six participants submitted Chinese system results, three Japanese, and six English for this pilot task. We plan to release the data to the research community, and hope to spur further research into cross-lingual opinion analysis and its use in other NLP tasks. In particular, we look forward to researchers using this data to investigate cross-cultural perspective differences based on automatic sentiment analysis.

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