In this paper we describe a new approach to extract element labels from Web form interfaces. Having these labels is a requirement for several techniques that attempt to retrieve and integrate information that is hidden behind form interfaces, such as hidden Web crawlers and metasearchers. However, given the wide variation in form layout, even within a well-defined domain, automatically extracting these labels is a challenging problem. Whereas previous approaches to this problem have relied on heuristics and manually specified extraction rules, our technique makes use of a learning classifier ensemble to identify element-label mappings; and it applies a reconciliation step which leverages the classifier-derived mappings to boost extraction accuracy. We present a detailed experimental evaluation using over three thousand Web forms. Our results show that our approach is effective: it obtains significantly higher accuracy and is more robust to variability in form layout than previous label extraction techniques.

The amplification of Coronavirus risk on social media sees Vietnam falling volatile to a chaotic sphere of mis/disinformation and incivility, which instigates a movement to counter its effects on public anxiety and fear. Benign or malign, these civil forces generate a huge public pressure to keep the one-party system on toes, forcing it to be unusually transparent in responding to public concerns.

Preventing the COVID-19 outbreak primarily depends on individuals' willingness to adopt social distancing and mask wearing behaviors. However, little is known about what drives individuals to adopt these behaviors. Guided by the Integrative Model of Behavioral Prediction, this study surveyed 590 adults in the US during the early stages of the outbreak to identify factors influencing intentions to practice social distancing and wear masks. Structural equation modeling results show that while attitudes are positively associated with intentions to perform both behaviors, perceived norms are positively associated with intentions to wear masks, and self-efficacy is positively associated with intentions to practice social distancing. Additionally, results indicate that adding personal risk perception and societal risk perception as distal variables increases the model's predictive power. Results reveal that while social risk perception is positively associated with attitudes, perceived norms, and self-efficacy for both behaviors, personal risk perception is negatively associated with attitudes toward mask wearing, and perceived norms and self-efficacy for both behaviors. Theoretical and practical implications are discussed.

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