Blog searching

Influencing Factors on College Students' Willingness to Spread Internet Public Opinion: Analysis Based on COVID-19 Data in China

Following COVID-19 outbreak, Internet public opinion has tended to proliferate. From a theoretical perspective, however, the spread law of Internet public opinion in major epidemic prevention and control may provide optimization strategies on how best to channel Internet public opinion. Specifically, this article aims at exploring key factors affecting our theoretical understanding on the spread of Internet public opinion on a major epidemic situation amongst college students. A questionnaire survey on college students was conducted via online research data collection platform located in Changsha, China, amassing three hundred and nineteen valid questionnaires. Smart PLS was applied to verify a theoretical model vis-à-vis the reliability and validity of the measuring instrument. Results show that adult attachment and social motivation have significant positive influences on the consciousness of social participation. Evidently, adult attachment, emotional orientation and risk perception also have significant positive influences on emotional motivation. Emotional motivation plays a mediating role in the relationship between affective disposition and dissemination willingness. Additionally, social motivation, consciousness of social participation and emotional motivation significantly influence one's dissemination willingness in a positive way. The consciousness of social participation plays a mediating role in the relationship between social motivation and dissemination willingness. Social motivation plays a moderating role in the relationship between risk perception and dissemination willingness. Altogether, theoretical rationalization to enhance understanding and guide the initiation and spread of Internet public opinion of major public health emergencies accurately has now been provided by this work.

OpinionML—Opinion Markup Language for Sentiment Representation

It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards “information symmetry”. It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

Infodemiology of alcohol use in Hong Kong mentioned on blogs: infoveillance study

BACKGROUND

In 2007 and 2008, the beer and wine tax in Hong Kong was halved and then abolished, resulting in an increase of alcohol consumption. The prevalence of the Internet and a high blogging rate by adolescents and adults present a unique opportunity to study drinking patterns by infodemiology.

OBJECTIVE

To assess and explain the online use of alcohol-related Chinese keywords and to validate blog searching as an infoveillance method for surveying changes in drinking patterns (eg, alcohol type) in Hong Kong people (represented by bloggers on a Hong Kong-based Chinese blogging site) in 2005-2010.

METHODS

Blog searching was done using a blog search engine, Google Blog Search, in the archives of a Hong Kong-based blog service provider, MySinaBlog from 2005-2010. Three groups of Chinese keywords, each representing a specific alcohol-related concept, were used: (1) "alcohol" (ie, the control concept), (2) "beer or wine", and (3) "spirit". The resulting blog posts were analyzed quantitatively using infodemiological metrics and correlation coefficients, and qualitatively by manual effort. The infodemiological metrics were (1) apparent prevalence, (2) actual prevalence, (3) prevalence rate, and (4) prevalence ratio. Pearson and Spearman correlations were calculated for prevalence rates and ratios with respect to per capita alcohol consumption. Manual analysis focused on (1) blog author characteristics (ie, authorship, sex, and age), and (2) blog content (ie, frequency of keywords, description of a discrete episode of alcohol drinking, drinking amount, and genres).

RESULTS

The online use of alcohol-related concepts increased noticeably for "alcohol" in 2008 and "spirit" in 2008-2009 but declined for "beer or wine" over the years. Correlation between infodemiological and epidemiological data was only significant for the "alcohol" prevalence rate. Most blogs were managed by single authors. Their sex distribution was even, and the majority were aged 18 and above. Not all Chinese keywords were found. Many of the blog posts did not describe a discrete episode of alcohol drinking and were classified as personal diary, opinion, or emotional outlets. The rest lacked information on drinking amount, which hindered assessment of binge drinking.

CONCLUSIONS

The prevalence of alcohol-related Chinese keywords online was attributed to many different factors, including spam, and hence not a specific reflection of local drinking patterns. Correlation between infodemiological data (represented by prevalence rates and ratios of alcohol-related concepts) and epidemiological data (represented by per capita alcohol consumption) was poor. Many blog posts were affective rather than informative in nature. Semantic analysis of blog content was recommended given enough expertise and resources.

Sentimental Spidering

Despite the increased prevalence of sentiment-related information on the Web, there has been limited work on focused crawlers capable of effectively collecting not only topic-relevant but also sentiment-relevant content. In this article, we propose a novel focused crawler that incorporates topic and sentiment information as well as a graph-based tunneling mechanism for enhanced collection of opinion-rich Web content regarding a particular topic. The graph-based sentiment (GBS) crawler uses a text classifier that employs both topic and sentiment categorization modules to assess the relevance of candidate pages. This information is also used to label nodes in web graphs that are employed by the tunneling mechanism to improve collection recall. Experimental results on two test beds revealed that GBS was able to provide better precision and recall than seven comparison crawlers. Moreover, GBS was able to collect a large proportion of the relevant content after traversing far fewer pages than comparison methods. GBS outperformed comparison methods on various categories of Web pages in the test beds, including collection of blogs, Web forums, and social networking Web site content. Further analysis revealed that both the sentiment classification module and graph-based tunneling mechanism played an integral role in the overall effectiveness of the GBS crawler.