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1.
《Information processing & management》2023,60(1):103146
The wide spread of fake news and its negative impacts on society has attracted a lot of attention to fake news detection. In existing fake news detection methods, particular attention has been paid to the credibility of the users sharing the news on social media, and the news sources based on their level of participation in fake news dissemination. However, these methods have ignored the important role of news topical perspectives (like political viewpoint) in users'/sources' decisions to share/publish the news. These decisions are associated with the viewpoints shared by the echo-chamber that the users belong to, i.e., users' Socio-Cognitive (SC) biases, and the news sources' partisan bias. Therefore, the credibility of users and news sources are varied in different topics according to the mentioned biases; which are completely ignored in current fake news detection studies. In this paper, we propose a Multi-View Co-Attention Network (MVCAN) that jointly models the latent topic-specific credibility of users and news sources for fake news detection. The key idea is to represent news articles, users, and news sources in a way that the topical viewpoints of news articles, SC biases of users which determines the users' viewpoints in sharing news, and the partisan bias of news sources are encoded as vectors. Then a novel variant of the Multi-Head Co-Attention (MHCA) mechanism is proposed to encode the joint interaction from different views, including news-source and news-user to implicitly model the credibility of users and the news sources based on their interaction in real and fake news spreading on the news topic. We conduct extensive experiments on two public datasets. The results show that MVCAN significantly outperforms other state-of-the-art methods and outperforms the best baselines by 3% on average in terms of F1 and Accuracy. 相似文献
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《Information processing & management》2022,59(1):102739
Digital information exchange enables quick creation and sharing of information and thus changes existing habits. Social media is becoming the main source of news for end-users replacing traditional media. This also enables the proliferation of fake news, which misinforms readers and is used to serve the interests of the creators. As a result, automated fake news detection systems are attracting attention. However, automatic fake news detection presents a major challenge; content evaluation is increasingly becoming the responsibility of the end-user. Thus, in the present study we used information quality (IQ) as an instrument to investigate how users can detect fake news. Specifically, we examined how users perceive fake news in the form of shorter paragraphs on individual IQ dimensions. We also investigated which user characteristics might affect fake news detection. We performed an empirical study with 1123 users, who evaluated randomly generated stories with statements of various level of correctness by individual IQ dimensions. The results reveal that IQ can be used as a tool for fake news detection. Our findings show that (1) domain knowledge has a positive impact on fake news detection; (2) education in combination with domain knowledge improves fake news detection; and (3) personality trait conscientiousness contributes significantly to fake news detection in all dimensions. 相似文献
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《Information processing & management》2020,57(6):102354
Nowadays, it is a common practice for healthcare professionals to spread medical knowledge by posting health articles on social media. However, promoting users’ intention to share such articles is challenging because the extent of sharing intention varies in their eHealth literacy (high or low) and the content valence of the article that they are exposed to (positive or negative). This study investigates boundary conditions under which eHealth literacy and content valence help to increase users’ intention to share by introducing a moderating role of confirmation bias—a tendency to prefer information that conforms to their initial beliefs. A 2 (eHealth literacy: high vs. low) × 2 (content valence: positive vs. negative) between-subjects experiment was conducted in a sample of 80 participants. Levels of confirmation bias ranging from extreme negative bias to extreme positive bias among the participants were assessed during the experiment. Results suggested that: (1) users with a high level of eHealth literacy were more likely to share positive health articles when they had extreme confirmation bias; (2) users with a high level of eHealth literacy were more likely to share negative health articles when they had moderate confirmation bias or no confirmation bias; (3) users with a low level of eHealth literacy were more likely to share health articles regardless of positive or negative content valence when they had moderate positive confirmation bias. This study sheds new light on the role of confirmation bias in users’ health information sharing. Also, it offers implications for health information providers who want to increase the visibility of their online health articles: they need to consider readers’ eHealth literacy and confirmation bias when deciding the content valence of the articles. 相似文献
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《Information processing & management》2019,56(5):1849-1864
Propaganda is a mechanism to influence public opinion, which is inherently present in extremely biased and fake news. Here, we propose a model to automatically assess the level of propagandistic content in an article based on different representations, from writing style and readability level to the presence of certain keywords. We experiment thoroughly with different variations of such a model on a new publicly available corpus, and we show that character n-grams and other style features outperform existing alternatives to identify propaganda based on word n-grams. Unlike previous work, we make sure that the test data comes from news sources that were unseen on training, thus penalizing learning algorithms that model the news sources used at training time as opposed to solving the actual task. We integrate our supervised model in a public website, which organizes recent articles covering the same event on the basis of their propagandistic contents. This allows users to quickly explore different perspectives of the same story, and it also enables investigative journalists to dig further into how different media use stories and propaganda to pursue their agenda. 相似文献
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以我国高新技术企业为例,对团队内成员间的知识分享行为及其影响因素进行实证研究。研究发现,团队成员间的共享心智模式能够引导成员在所需的情境中与其他成员进行互动,对知识分享行为具有显著的正向影响效果,而这种影响效果会受到成员性格特征的调节影响。因此在团队运作过程中,团队领导者若能通过详细的任务说明以及增进成员的互动等方式,让不同性格的团队成员具有相匹配的心智模式,将有助于团队知识分享行为的产生。 相似文献
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《Information processing & management》2020,57(6):102315
In this work, we release a multi-domain and multi-modality event dataset (MMED), containing 25,052 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, BBC News, etc.) and 75,884 image posts shared on Flickr by thousands of social media users. The articles contributed by professional journalists and the images shared by amateur users are annotated according to 410 real-world events, covering emergencies, natural disasters, sports, ceremonies, elections, protests, military intervention, economic crises, etc. The MMED dataset is collected by the following the principles of high relevance in supporting the application needs, a wide range of event types, non-ambiguity of the event labels, imbalanced event clusters, and difficulty discriminating the event labels. The dataset can stimulate innovative research on related challenging problems, such as (weakly aligned) cross-modal retrieval and cross-domain event discovery, inspire visual relation mining and reasoning, etc. For comparisons, 15 baselines for two scenarios have been quantitatively and qualitatively evaluated using the dataset. 相似文献
7.
News source evaluations based on fact-checking can help curb the consumption and spread of fake news on social media. Prior research has primarily considered source evaluations with intuitive icons that indicate whether or not news sources are reputable. But can we increase the power of these icons by adding more detailed information about the evaluation that explains the reasons for the icon? What additional benefit would such evaluation details bring? Would they have the same effect for both positive and negative evaluations? We conducted two online experiments to understand the effects of a source evaluation icon (a positive or negative summary of the evaluation) and more detailed evaluation information explaining the reasons for the icon. Our results show an asymmetric effect of positive and negative icons and details. Negative icons reduced the believability of the articles, but adding evaluation details supporting the icon had no additional effect. In contrast, positive icons had no significant effects, but adding evaluation details significantly increased believability. We also found that users were more likely to view the evaluation details when the content of the article aligned with their pre-existing opinions, but the valence of the icon (positive or negative) did not affect this decision. 相似文献
8.
Journalists, emergency responders, and the general public use Twitter during disasters as an effective means to disseminate emergency information. However, there is a growing concern about the credibility of disaster tweets. This concern negatively influences Twitter users’ decisions about whether to retweet information, which can delay the dissemination of accurate—and sometimes essential—communications during a crisis. Although verifying information credibility is often a time-consuming task requiring considerable cognitive effort, researchers have yet to explore how people manage this task while using Twitter during disaster situations.To address this, we adopt the Heuristic-Systematic Model of information processing to understand how Twitter users make retweet decisions by categorizing tweet content as systematically processed information and a Twitter user’s profile as heuristically processed information. We then empirically examine tweet content and Twitter user profiles, as well as how they interact to verify the credibility of tweets collected during two disaster events: the 2011 Queensland floods, and the 2013 Colorado floods. Our empirical results suggest that using a Twitter profile as source-credibility information makes it easier for Twitter users to assess the credibility of disaster tweets. Our study also reveals that the Twitter user profile is a reliable source of credibility information and enhances our understanding of timely communication on Twitter during disasters. 相似文献
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《Information processing & management》2023,60(2):103177
In order to evaluate the effectiveness of Information Retrieval (IR) systems it is key to collect relevance judgments from human assessors. Crowdsourcing has successfully been used as a method to scale-up the collection of manual relevance judgments, and previous research has investigated the impact of different judgment task design elements (e.g., highlighting query keywords in the document) on judgment quality and efficiency. In this work we investigate the positive and negative impacts of presenting crowd human assessors with more than just the topic and the document to be judged. We deploy different variants of crowdsourced relevance judgment tasks following a between-subjects design in which we present different types of metadata to the human assessor. Specifically, we investigate the effect of human metadata (e.g., what other human assessors think of the current document, as in which relevance level has already been selected by the majority crowd workers), machine metadata (e.g., how IR systems scored this document such as its average position in ranked lists, statistics about the document such as term frequencies). We look at the impact of metadata on judgment quality (i.e., the level of agreement with trained assessors) and cost (i.e., the time it takes for workers to complete the judgments) as well as at how metadata quality positively or negatively impact the collected judgments. 相似文献
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This study examines the extent to which politicians' visibility in traditional news coverage explains individual politicians' visibility on social media, and vice versa. We also explore whether these relationships depend on commonly identified characteristics of individual politicians. We collected data for all elected candidates from the 2012 Dutch national elections covering each 15 days prior to the election day (N = 2250). This includes 2736 newspaper articles and 77,597 mentions on Facebook and Twitter. Our results show that the traditional news agenda and social media agenda impact each other, but that the reciprocal influence is not independent of politician characteristics. 相似文献
12.
《Information processing & management》2020,57(1):102106
Forwarding negative information on microblogs, termed reposting negative information (RNI) in this study, refers to reposting negative, non-original information publicly on microblogs, causes large-scale bad news dissemination on microblogs, which in turn has detrimental consequences for organizations and the society. However, previous research concentrated on sharing of original content (such as knowledge sharing and word-of-mouth) or focused on general information forwarding on social media without distinguishing between positive and negative information. To address this issue, this study develops a model to investigate the predictors of RNI on microblogs (negative emotions and issue involvement in this study) and explores the contingency role of personality. A scenario-based online survey was conducted to test the proposed model and hypotheses. The empirical results confirmed (1) the direct and positive effects of negative emotions and issue involvement, (2) the negative moderation effect of extraversion on the relationship between negative emotions and RNI, and (3) the positive moderation effects of conscientiousness and agreeableness on the relationship between issue involvement and RNI. The study contributes to the literature by revealing the predictors of RNI on microblogs and by investigating the contingency role of personality. 相似文献
13.
Victor Makarenkov Ido Guy Niva Hazon Tamar Meisels Bracha Shapira Lior Rokach 《Information processing & management》2019,56(5):1880-1893
In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers.We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user’s knowledge, intent or belief that may be based on writer’s moral foundation: (1) political perspective detection in news articles (2) identification of informational vs. conversational questions in community question answering (CQA) archives. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press. 相似文献
14.
《Information processing & management》2023,60(2):103206
A news article’s online audience provides useful insights about the article’s identity. However, fake news classifiers using such information risk relying on profiling. In response to the rising demand for ethical AI, we present a profiling-avoiding algorithm that leverages Twitter users during model optimisation while excluding them when an article’s veracity is evaluated. For this, we take inspiration from the social sciences and introduce two objective functions that maximise correlation between the article and its spreaders, and among those spreaders. We applied our profiling-avoiding algorithm to three popular neural classifiers and obtained results on fake news data discussing a variety of news topics. The positive impact on prediction performance demonstrates the soundness of the proposed objective functions to integrate social context in text-based classifiers. Moreover, statistical visualisation and dimension reduction techniques show that the user-inspired classifiers better discriminate between unseen fake and true news in their latent spaces. Our study serves as a stepping stone to resolve the underexplored issue of profiling-dependent decision-making in user-informed fake news detection. 相似文献
15.
Described here is a study of how students actively read electronic journal papers to prepare for classroom discussions. Eighteen students enrolled in a graduate course participated in this study; half of them read the documents privately, while the other half shared their readings. These readers were digitally monitored as they read, annotated, and shared the electronic (e-) documents over a course of several weeks during a semester. This monitoring yielded a comprehensive data bank of 60 e-documents (with 1923 markings), and 56 computer logs. Using semi-structured interviews, the reading, marking, and navigational activities of the participating readers were analyzed in detail. Under scrutiny were a range of activities that the subjects carried out. Analyses of the data revealed the types of markings that the users employ, and the ways in which those marking were placed. A derivation of the user-perceived functions of the marking structures was then carried out. The findings then lead to several implications for informing the design of reading and marking applications in digital libraries. 相似文献
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[目的/意义] 开放式创新社区能够帮助企业获取外部用户分享的知识并从中筛选出具有价值的创意。但用户往往缺乏分享的动机和意愿,这将导致社区运营的失败。因此有必要研究开放式创新社区用户知识分享的影响因素。[方法/过程] 基于社会认知理论构建研究模型,并以国内典型的开放式创新社区——MIUI论坛为对象,在线抓取数据并采用统计软件Stata进行分析。[结果/结论] 结果表明,创新自我效能、结果期望、社会认同和社区影响对用户知识分享行为均有显著正向影响,用户经验对创新自我效能有负向调节作用,对结果期望有正向调节作用,对社会认同和社区影响有部分调节作用。 相似文献
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《Information processing & management》2020,57(1):102126
We analyze a data-set including more than 4.5 million tweets related to four highly emotional riot events. In particular, we examine statistically significant structural patterns that emerge as humans directly engage in an exchange of emotional messages with other humans on Twitter. Furthermore, we compare typical human-to-human communication patterns with those that emerge as bots engage in an emotional message-exchange with human users. To this end, we apply the novel concept of emotion-exchange motifs. We found that a) human-to-human conversations results in a variety of motifs that contain reciprocal edges and self-loops, b) bots predominantly contribute to the emergence of message broadcasting, single-way message sending behavior, c) in contrast to previous findings we found that in certain events bots frequently engage in direct message exchanges with humans, d) during riot events bots tend to direct fear-conveying messages to human users. 相似文献
20.
Roi Blanco Gianmarco De Francisci Morales Fabrizio Silvestri 《Information processing & management》2015
We present IntoNews, a system to match online news articles with spoken news from a television newscasts represented by closed captions. We formalize the news matching problem as two independent tasks: closed captions segmentation and news retrieval. The system segments closed captions by using a windowing scheme: sliding or tumbling window. Next, it uses each segment to build a query by extracting representative terms. The query is used to retrieve previously indexed news articles from a search engine. To detect when a new article should be surfaced, the system compares the set of retrieved articles with the previously retrieved one. The intuition is that if the difference between these sets is large enough, it is likely that the topic of the newscast currently on air has changed and a new article should be displayed to the user. In order to evaluate IntoNews, we build a test collection using data coming from a second screen application and a major online news aggregator. The dataset is manually segmented and annotated by expert assessors, and used as our ground truth. It is freely available for download through the Webscope program.1 Our evaluation is based on a set of novel time-relevance metrics that take into account three different aspects of the problem at hand: precision, timeliness and coverage. We compare our algorithms against the best method previously proposed in literature for this problem. Experiments show the trade-offs involved among precision, timeliness and coverage of the airing news. Our best method is four times more accurate than the baseline. 相似文献