User comments for news recommendation in forum-based social media

News recommendation and user interaction are important features in many Web-based news services. The former helps users identify the most relevant news for further information. The latter enables collaborated information sharing among users with their comments following news postings. This research is intended to marry these two features together for an adaptive recommender system that utilizes reader comments to refine the recommendation of news in accordance with the evolving topic. This then turns the traditional “push-data” type of news recommendation to “discussion” moderator that can intelligently assist online forums. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplicate, generalization, or specialization) between recommended news articles and the original posting is investigated. Our experiments indicate that our proposed solutions provide an improved news recommendation service in forum-based social media.     

PENETRATE: Personalized news recommendation using ensemble hierarchical clustering

Recommending online news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Many online readers have their own reading preference on news articles; however, a group of users might be interested in similar fascinating topics. It would be helpful to take into consideration the individual and group reading behavior simultaneously when recommending news items to online users. In this paper, we propose PENETRATE, a novel PErsonalized NEws recommendaTion framework using ensemble hieRArchical clusTEring to provide attractive recommendation results. Specifically, given a set of online readers, our approach initially separates readers into different groups based on their reading histories, where each user might be designated to several groups. Once a collection of newly-published news items is provided, we can easily construct a news hierarchy for each user group. When recommending news articles to a given user, the hierarchies of multiple user groups that the user belongs to are merged into an optimal one. Finally a list of news articles are selected from this optimal hierarchy based on the user’s personalized information, as the recommendation result. Extensive empirical experiments on a set of news articles collected from various popular news websites demonstrate the efficacy of our proposed approach.     

Third-person perception of online comments: Civil ones persuade you more than me

Using online comments posted on news stories as the context, this study aimed to examine the interplay between the third-person perception (TPP) – that people believe media message have a greater effect on changing the attitudes of others compared with themselves – and online incivility. It also examined whether people's agreement with the content of the comments would influence the TPP. Results of an experiment (N = 301) showed incivility muted the persuasive effect of online comments, so only civil comments produced a TPP, whereby people felt comments had greater persuasive power over others compared with themselves. However, counter to predictions, whether people agreed with the comments did not influence the TPP. Findings also supported the TPP social distance corollary such that subjects perceived comments as having the largest third-person perceptual gap between the self and those who disagreed with them. Results are discussed in relation to TPP and face and politeness theories.     

A joint model for analyzing topic and sentiment dynamics from large-scale online news

Many of today’s online news websites and aggregator apps have enabled users to publish their opinions without respect to time and place. Existing works on topic-based sentiment analysis of product reviews cannot be applied to online news directly because of the following two reasons: (1) The dynamic nature of news streams require the topic and sentiment analysis model also to be dynamically updated. (2) The user interactions among news comments can easily lead to inaccurate topic extraction and sentiment classification. In this paper, we propose a novel probabilistic generative model (DTSA) to extract topics and the specified sentiments from news streams and analyze their evolution over time simultaneously. In DTSA, three different timescale models are studied to account for the historical dependencies of sentiment-topic word distributions at current epoch, continuous, skip and multiple timescale models. Additionally, we further consider the links among news comments to avoid the error caused by user interactions. In order to mine more interpretable topics, a Conditional Random Fields (CRF) model is adopted to label a set of meaningful phrases for augmenting the bag-of-word features. Finally, we derive distributed online inference procedures to update the model with newly arrived data and show the effectiveness of our proposed model on real-world data sets.     

Dangerous minds? Effects of uncivil online comments on aggressive cognitions,emotions, and behavior

Despite the potential of online commenting spaces for public deliberation, they are often full of destructive or uncivil and aggressive comments. Based on research on social learning and social influence, we conducted an online experiment to investigate the effects of uncivil comments on readers' cognitive, emotional, and behavioral reactions. The experiment was built on a one-factorial between-subjects design including four experimental conditions and a control group: Participants were exposed to a news article and six user comments of which zero, one, three, or six were uncivil. Results suggest that exposure to uncivil comments can lead to an increase in readers' hostile cognitions. The effect, however, does not rise with exposure to a higher proportion of incivility. No significant effects were found on hostile emotions or the use of incivility in readers' own comments.     

Posting,commenting, and tagging: Effects of sharing news stories on Facebook

Social networking sites such as Facebook provide new ways of sharing news stories that allow users to act as opinion leaders in their networks, encourage discussion, and potentially increase their involvement in current events. This study identifies the particular features of Facebook that facilitate the discussion of news and tests their effects on involvement and feelings of influence. Participants (N = 265) in a 3 (Broadcast level: news feed vs. wall post vs. direct message) × 3 (Elaboration: opinion vs. question vs. no comment) × 2 (Involving-friends: tag vs. no tag) between-subjects factorial experiment were randomly assigned to share a story from a news website on Facebook. Results show that user involvement in the news content depends on the social affordances of the site, particularly those that allow for audience customization and those that drive network feedback. Asking the network’s opinions and targeting specific friends led to greater involvement in the news content. Discussion through comments led to a greater sense of influence and greater involvement for those sharing the news story. These findings highlight the importance of encouraging individuals to act as sources of information in their networks to drive engagement in current events in the changing news landscape.     

Inside the spiral: Hostile media,minority perception,and willingness to speak out on a weblog

This study took a snapshot at the psychological process of spiral of silence in an online environment with South Korean college students as the study participants. In an experiment, a Weblog-embedded news article from a media source incongruent with one's political orientation, as opposed to the same article from a congruent media source, triggered hostile media perception, which in turn led to presumed influence of the online news article on others. The presumed influence on others became a basis for assessing the online climate of opinion and led people to view themselves as a minority against the online climate of opinion. The media source factor, in conjunction with user comments factor, also generated different levels of minority perception both online and in the general society. In contrast to the notion of spiral of silence, minority perception against the general society's climate of opinion increased rather than decreased willingness to speak out.     

Private Self-Awareness and Aggression in Computer-Mediated Communication: Abusive User Comments on Online News Articles

Although there have been previous studies in social psychology on the association between self-awareness and aggression, none has investigated the relationship in computer-mediated communication (CMC). The present study aims to replicate the findings in the context of CMC, by replacing the traditional mirror manipulation of private self-awareness with a real-time video image of oneself, and by measuring the intention of verbal aggression in abusive user comments on online news articles. As predicted, two experiments (N = 163) found that participants were less likely to agree with abusive comments and intend to write such comments when private self-awareness is induced. The effects of device size, self-monitoring, and social norm were also examined. In addition to extending theories of self-awareness to the context of CMC, the study offers a novel approach to addressing incivility on the Internet.     

Commenting in the Online Arab Public Sphere: Debating the Swiss Minaret Ban and the “Ground Zero Mosque” Online

This article focuses on the emerging online Arab public sphere that the web has enabled. It explores how the “local” interacts with the “global,” critically examining their implications on global politics. Specifically looking at online readers' comments about the Swiss minaret ban the so‐called “Ground Zero Mosque,” how does the Arab online public sphere respond to and frame these issues? What implications do these reader frames portend locally and globally? The study analyzes online comments and responses that readers of Al Arabiya.net and Al Jazeera.net posted on related news articles. The article concludes that the new online public sphere does make it possible for Arab citizens to circumvent and challenge traditional authoritarian controls.     

Echo chambers online?: Politically motivated selective exposure among Internet news users1

A review of research suggests that the desire for opinion reinforcement may play a more important role in shaping individuals’ exposure to online political information than an aversion to opinion challenge. The article tests this idea using data collected via a web‐administered behavior‐tracking study with subjects recruited from the readership of 2 partisan online news sites (N = 727). The results demonstrate that opinion‐reinforcing information promotes news story exposure while opinion‐challenging information makes exposure only marginally less likely. The influence of both factors is modest, but opinion‐reinforcing information is a more important predictor. Having decided to view a news story, evidence of an aversion to opinion challenges disappears: There is no evidence that individuals abandon news stories that contain information with which they disagree. Implications and directions for future research are discussed.     

An attention-based deep learning model for credibility assessment of online health information

With the surge of searching and reading online health-based articles, maintaining the quality and credibility of online health-based articles has become crucial. The circulation of deceptive health information on numerous social media sites can mislead people and can potentially cause adverse effects on people's health. To address these problems, this work uses deep learning approaches to automate the assessment and scoring of online health-related articles' credibility. The paper proposed an Attention-based Recurrent Multichannel Convolutional Neural Network (ARMCNN) model. The proposed model incorporates a BiLSTM layer, a multichannel CNN layer, and an attention layer and predicts the credibility of online health information. To perform a reliable evaluation of the presented model, we utilize the health articles reviewed by the experts, compiled in a labeled dataset termed “Pubhealth,” which consists of thousands of health articles. The results are evaluated using five performance measures, accuracy, precision, recall, f1-score, and area under the ROC curve (AUC). Furthermore, we extensively compared the proposed model with different deep learning and machine learning models such as Long short-term memory (LSTM), Bidirectional LSTM, CNN (Convolutional neural network), and RNN-CNN. The experimental results showed that the proposed model produced state-of-the-art performance on the used dataset by achieving an accuracy of 0.88, precision of 0.92, recall of 0.87, f1-score of 0.90, and AUC of 0.94. Further, the proposed model yielded better performance than other benchmarked techniques for the credibility assessment of online health articles.     

基于群体行为的社交媒体中水军分析

社交媒体是当下非常热门的一种互联网应用。在发生公共事件时,许多当事人雇佣公关公司在社交媒体上发布虚假信息来影响舆情。这部分为公关公司所服务的社交媒体账号称为水军。水军在当下的中国社交媒体中有泛滥的趋势。基于水军的群体行为分析社交媒体中的水军集团。定义了情感社会网络和情感社会网络中的团的概念;设计了一个分析框架,包括提出了情感社会网络中发现社区,以及从社区中发现对立的派别（称为团）的算法。从微博的评论数据构建情感社会网络,并发现情感社会网络中的团,可以识别水军,而且可以考察社交媒体中的水军集团的特性。采用人工标注的数据集进行了实验。     

Modeling and broadening temporal user interest in personalized news recommendation

User profiling is an important step for solving the problem of personalized news recommendation. Traditional user profiling techniques often construct profiles of users based on static historical data accessed by users. However, due to the frequent updating of news repository, it is possible that a user’s fine-grained reading preference would evolve over time while his/her long-term interest remains stable. Therefore, it is imperative to reason on such preference evaluation for user profiling in news recommenders. Besides, in content-based news recommenders, a user’s preference tends to be stable due to the mechanism of selecting similar content-wise news articles with respect to the user’s profile. To activate users’ reading motivations, a successful recommender needs to introduce “somewhat novel” articles to users.In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that a user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen as the recommended candidates based on the short-term user profile. We further propose to select news items from the user–item affinity graph using absorbing random walk model to increase the diversity of the recommended news list. Extensive empirical experiments on a collection of news data obtained from various popular news websites demonstrate the effectiveness of our method.     

Disentangling rhetorical subarenas of public health crisis communication: A study of the 2014–2015 Ebola outbreak in the news media and social media in Sweden

Drawing on rhetorical arena theory, this study investigates the variations of content across communication channels (subarenas), guided by different media logics, during the 2014–2015 Ebola outbreak. Restricting the study to one country, Sweden, a content analysis was conducted of two national newspapers (in total 848 articles), their posts on branded Facebook pages (47) and user comments on these Facebook posts (1,661). Some conclusions to be made are, that content differed between subarenas, with Facebook news being more sensational and focused on human interest stories, and Facebook comments to a greater extent related to other current socio‐political issues. Also, news media were more alarmist than Facebook comments, although alarmism declined on all subarenas as the danger became more tangible.     

An empirical investigation of electronic word-of-mouth: Informational motive and corporate response strategy

The proliferation of the Internet has given birth to a number of complaint web sites where dissatisfied and frustrated consumers can easily articulate their opinions and comments on products, services, or companies. Little attention, however, has been paid to the influence of online complaints on potential consumers’ behaviors. This study attempts to provide the understanding of causal attribution process in the online complaining behaviors. The results showed that informational factors, such as vividness and consensus, facilitated consumers’ attribution to companies’ responsibility for the negative events, and subsequently led to changing their evaluation of the companies. In addition, we found that corporate response strategies to online complaints should be different from the conventional response strategies.     

Triggering participation: Exploring the effects of third-person and hostile media perceptions on online participation

Using a 2 × 3 mixed between-within subjects experiment (N = 102), we tested how the presence of online comments affects self-other differences and perceptions of media bias, as well as factors predicting subjects’ likelihood of commenting on an online news story. We found that (a) presence of comments lowers self-other differences and consequently attenuates the third-person effect, and (b) perceptions of media bias significantly predict likelihood of commenting. Additionally, we found that subjects were more likely to comment on stories they found biased against their position as a form of corrective action, and that subjects were more likely to share and like stories they found biased in favor of their position as a form of promotional action.     

MONERS: A news recommender for the mobile web

Mobile web news services, which served by mobile service operators collecting news articles from diverse news contents providers, provide articles sorted by category or on the basis of attributes, such as the time at which they were posted. The mobile web should provide easy access to the categories or news contents preferred by users because user interface of wireless devices, particularly cell phones is limited for browsing between contents.This paper presents a mobile web news recommendation system (MONERS) that incorporates news article attributes and user preferences with regard to categories and news articles. User preference of news articles are estimated by aggregating news article importance and recency, user preference change, and user segment’s preference on news categories and articles. Performance of MONERS was tested in an actual mobile web environment; news organized by category had more page hits, while recommended news had a higher overall article read ratio.     

Social media metrics: Third-person perceptions of health information

The present study investigated the role of social media metrics (i.e., number of shares and comments) displayed alongside online news stories in shaping users’ perceptions of the content and its influence. In a web-based experiment (N = 144), participants first read a cancer news story that displayed either a high or a low level of social media metrics, then reported their perceived story influence on the self and others, as well as their behavioral intentions. In the low social media metrics condition, the general story influence was perceived to be stronger for others than for the self, indicative of the “third-person effect.” This effect, however, was diminished to insignificant levels in the high social media metrics condition. Further, social media metrics had an ultimate indirect effect on behavioral intentions via the third-person effect. Theoretical and practical implications of these findings are provided in the end.     

Attention to news and its dissemination on Twitter: A survey

In recent years, news media have been hugely disrupted by news promotion, commentary and sharing in online, social media (e.g., Twitter, Facebook, and Reddit). This disruption has been the subject of a significant literature that has largely used AI techniques – machine learning, text analytics and network models – to both (i) understand the factors underlying audience attention and news dissemination on social media (e.g., effects of popularity, type of day) and (ii) provide new tools/guidelines for journalists to better disseminate their news via these social media. This paper provides an integrative review of the literature on the professional reporting of news on Twitter; focusing on how journalists and news outlets use Twitter as a platform to disseminate news, and on the factors that impact readers’ attention and engagement with that news on Twitter. Using the precise definition of a news-tweet (i.e., divided into user, content and context features), the survey structures the literature to reveal the main findings on features affecting audience attention to news and its dissemination on Twitter. From this analysis, it then considers the most effective guidelines for digital journalists to better disseminate news in the future.