-
Son, J.; Lee, J.; Larsen, I.; Nissenbaum, K.R.; Woo, J.: Understanding the uncertainty of disaster tweets and its effect on retweeting : the perspectives of uncertainty reduction theory and information entropy (2020)
0.00
0.0029433446 = product of:
0.0058866893 = sum of:
0.0058866893 = product of:
0.011773379 = sum of:
0.011773379 = weight(_text_:a in 5962) [ClassicSimilarity], result of:
0.011773379 = score(doc=5962,freq=30.0), product of:
0.04772363 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.041389145 = queryNorm
0.24669915 = fieldWeight in 5962, product of:
5.477226 = tf(freq=30.0), with freq of:
30.0 = termFreq=30.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=5962)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- The rapid and wide dissemination of up-to-date, localized information is a central issue during disasters. Being attributed to the original 140-character length, Twitter provides its users with quick-posting and easy-forwarding features that facilitate the timely dissemination of warnings and alerts. However, a concern arises with respect to the terseness of tweets that restricts the amount of information conveyed in a tweet and thus increases a tweet's uncertainty. We tackle such concerns by proposing entropy as a measure for a tweet's uncertainty. Based on the perspectives of Uncertainty Reduction Theory (URT), we theorize that the more uncertain information of a disaster tweet, the higher the entropy, which will lead to a lower retweet count. By leveraging the statistical and predictive analyses, we provide evidence supporting that entropy validly and reliably assesses the uncertainty of a tweet. This study contributes to improving our understanding of information propagation on Twitter during disasters. Academically, we offer a new variable of entropy to measure a tweet's uncertainty, an important factor influencing disaster tweets' retweeting. Entropy plays a critical role to better comprehend URLs and emoticons as a means to convey information. Practically, this research suggests a set of guidelines for effectively crafting disaster messages on Twitter.
- Type
- a
-
Lee, J.; Jatowt, A.; Kim, K.-S..: Discovering underlying sensations of human emotions based on social media (2021)
0.00
0.0018615347 = product of:
0.0037230693 = sum of:
0.0037230693 = product of:
0.0074461387 = sum of:
0.0074461387 = weight(_text_:a in 163) [ClassicSimilarity], result of:
0.0074461387 = score(doc=163,freq=12.0), product of:
0.04772363 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.041389145 = queryNorm
0.15602624 = fieldWeight in 163, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=163)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Analyzing social media has become a common way for capturing and understanding people's opinions, sentiments, interests, and reactions to ongoing events. Social media has thus become a rich and real-time source for various kinds of public opinion and sentiment studies. According to psychology and neuroscience, human emotions are known to be strongly dependent on sensory perceptions. Although sensation is the most fundamental antecedent of human emotions, prior works have not looked into their relation to emotions based on social media texts. In this paper, we report the results of our study on sensation effects that underlie human emotions as revealed in social media. We focus on the key five types of sensations: sight, hearing, touch, smell, and taste. We first establish a correlation between emotion and sensation in terms of linguistic expressions. Then, in the second part of the paper, we define novel features useful for extracting sensation information from social media. Finally, we design a method to classify texts into ones associated with different types of sensations. The sensation dataset resulting from this research is opened to the public to foster further studies.
- Type
- a
-
Liu, M.; Bu, Y.; Chen, C.; Xu, J.; Li, D.; Leng, Y.; Freeman, R.B.; Meyer, E.T.; Yoon, W.; Sung, M.; Jeong, M.; Lee, J.; Kang, J.; Min, C.; Zhai, Y.; Song, M.; Ding, Y.: Pandemics are catalysts of scientific novelty : evidence from COVID-19 (2022)
0.00
0.0016993409 = product of:
0.0033986818 = sum of:
0.0033986818 = product of:
0.0067973635 = sum of:
0.0067973635 = weight(_text_:a in 633) [ClassicSimilarity], result of:
0.0067973635 = score(doc=633,freq=10.0), product of:
0.04772363 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.041389145 = queryNorm
0.14243183 = fieldWeight in 633, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=633)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.
- Type
- a
