Adversarial Cross-domain Community Question Retrieval

Author(s):  
Aibo Guo ◽  
Xinyi Li ◽  
Ning Pang ◽  
Xiang Zhao

Community Q&A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a question retrieval function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely, X-QR , which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&A forums question retrieval. The experiment results suggest that the proposed X-QR model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&A tasks.

2021 ◽  
Vol 27 (3) ◽  
pp. 146045822110214
Author(s):  
Oberiri Destiny Apuke ◽  
Bahiyah Omar

This study modelled factors that predict fake news sharing during the COVID-19 health crisis using the perspective of the affordance and cognitive load theory. Data were drawn from 385 social media users in Nigeria, and Partial Least Squares (PLS) was used to analyse the data. We found that news-find-me perception, information overload, trust in online information, status seeking, self-expression and information sharing predicted fake news sharing related to COVID-19 pandemic among social media users in Nigeria. Greater effects of news-find-me perception and information overload were found on fake news sharing behaviour as compared to trust in online information, status seeking, self-expression and information sharing. Theoretically, our study enriches the current literature by focusing on the affordances of social media and the abundance of online information in predicting fake news sharing behaviour among social media users, especially in Nigeria. Practically, we suggest intervention strategies which nudge people to be sceptical of the information they come across on social media.


Author(s):  
Michael Atighetchi ◽  
Jonathan Webb ◽  
Partha Pal ◽  
Joseph Loyall ◽  
Azer Bestavros ◽  
...  

2021 ◽  
Vol 66 (1) ◽  
pp. 180-192
Author(s):  
Carmen Mª Cedillo Corrochano

Abstract Public Service Interpreting and Translation –PSIT– is a specialty of the studies of Translation and Interpreting that generates controversy in the specialized literature in its most basic defining aspects. For this reason, a reading of the literature will reveal a lack of consensus in its own conceptualisation; something essential for its social and professional knowledge/acknowledgment. Thus, this article will focus on the denominational plurality of the PSIT in Spain and will offer a quali-quantitative analysis of the names under which it is known in Spain and the use of these names in two of the most popular social media in Spanish society today: Twitter and YouTube.


2020 ◽  
pp. 109019812098476
Author(s):  
Linqi Lu ◽  
Jiawei Liu ◽  
Y. Connie Yuan ◽  
Kelli S. Burns ◽  
Enze Lu ◽  
...  

Health information sharing has become especially important during the COVID-19 (coronavirus disease 2019) pandemic because people need to learn about the disease and then act accordingly. This study examines the perceived trust of different COVID-19 information sources (health professionals, academic institutions, government agencies, news media, social media, family, and friends) and sharing of COVID-19 information in China. Specifically, it investigates how beliefs about sharing and emotions mediate the effects of perceived source trust on source-specific information sharing intentions. Results suggest that health professionals, academic institutions, and government agencies are trusted sources of information and that people share information from these sources because they think doing so will increase disease awareness and promote disease prevention. People may also choose to share COVID-19 information from news media, social media, and family as they cope with anxiety, anger, and fear. Taken together, a better understanding of the distinct psychological mechanisms underlying health information sharing from different sources can help contribute to more effective sharing of information about COVID-19 prevention and to manage negative emotion contagion during the pandemic.


Author(s):  
Tseng-Hung Chen ◽  
Yuan-Hong Liao ◽  
Ching-Yao Chuang ◽  
Wan-Ting Hsu ◽  
Jianlong Fu ◽  
...  

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