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1. Public Reaction to Scientific Research via Twitter Sentiment Prediction

   https://doi.org/10.2478/jdis-2022-0003  

   Shahzad, Murtuza ; Alhoori, Hamed ( December 2021 , Journal of Data and Information Science)

   Abstract Purpose Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles. Design/methodology/approach Several tools are used for sentiment analysis, so we applied five sentiment analysis tools to check which are suitable for capturing a tweet's sentiment value and decided to use NLTK VADER and TextBlob. We segregated the sentiment value into negative, positive, and neutral. We measure the mean and median of tweets’ sentiment value for research articles with more than one tweet. We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models. Findings We found that the most important feature in all the models was the sentiment of the research article title followed by the author count. We observed that the tree-based models performed bettermore »than other classification models, with Random Forest achieving 89% accuracy for binary classification and 73% accuracy for three-label classification. Research limitations In this research, we used state-of-the-art sentiment analysis libraries. However, these libraries might vary at times in their sentiment prediction behavior. Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper's details. In the future, we intend to broaden the scope of our research by employing word2vec models. Practical implications Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes. Research in this area has relied on fewer and more limited measures, such as citations and user studies with small datasets. There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research. This study will help scientists better comprehend the emotional impact of their work. Additionally, the value of understanding the public's interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public. Originality/value This study will extend work on public engagement with science, sociology of science, and computational social science. It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.« less
2. Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

   https://doi.org/10.2196/30765  

   Monselise, Michal ; Chang, Chia-Hsuan ; Ferreira, Gustavo ; Yang, Rita ; Yang, Christopher C ( January 2021 , Journal of Medical Internet Research)

   Background As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as wellmore »as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.« less
3. A Technology-Based Pregnancy Health and Wellness Intervention (Two Happy Hearts): Case Study

   https://doi.org/10.2196/30991  

   Jimah, Tamara ; Borg, Holly ; Kehoe, Priscilla ; Pimentel, Pamela ; Turner, Arlene ; Labbaf, Sina ; Asgari Mehrabadi, Milad ; Rahmani, Amir M. ; Dutt, Nikil ; Guo, Yuqing ( January 2021 , JMIR Formative Research)

   Background The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement. Objective The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user’s engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components. Methods A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotionalmore »health using the Two Happy Hearts mobile app–based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data. Results Decreased resting heart rate was significantly correlated with increased heart rate variability (r=–0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery. Conclusions The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women.« less
4. Cues conditioned to withdrawal and negative reinforcement: Neglected but key motivational elements driving opioid addiction

   https://doi.org/10.1126/sciadv.abf0364  

   Pantazis, Caroline B. ; Gonzalez, Luis A. ; Tunstall, Brendan J. ; Carmack, Stephanie A. ; Koob, George F. ; Vendruscolo, Leandro F. ( April 2021 , Science Advances)

   Opioid use disorder (OUD) is a debilitating disorder that affects millions of people. Neutral cues can acquire motivational properties when paired with the positive emotional effects of drug intoxication to stimulate relapse. However, much less research has been devoted to cues that become conditioned to the aversive effects of opioid withdrawal. We argue that environmental stimuli promote motivation for opioids when cues are paired with withdrawal (conditioned withdrawal) and generate opioid consumption to terminate conditioned withdrawal (conditioned negative reinforcement). We review evidence that cues associated with pain drive opioid consumption, as patients with chronic pain may misuse opioids to escape physical and emotional pain. We highlight sex differences in withdrawal-induced stress reactivity and withdrawal cue processing and discuss neurocircuitry that may underlie withdrawal cue processing in dependent individuals. These studies highlight the importance of studying cues associated with withdrawal in dependent individuals and point to areas for exploration in OUD research.
5. Evidence for Phosphorylation-Dependent, Dynamic, Regulation of mGlu5 and Homer2 in Expression of Cocaine Aversion in Mice

   https://doi.org/10.1523/ENEURO.0423-22.2023  

   Szumlinski, Karen K. ; Beltran, Jacqueline ; van Doren, Eliyana ; Jimenez Chavez, C. Leonardo ; Domingo-Gonzalez, Racquel D. ; Reyes, Cindy M. ; Ary, Alexis W. ; Lang, Andrew ; Guo, Weiruo ; Worley, Paul F. ; et al ( March 2023 , eneuro)

   Cocaine-induced changes in the expression of the glutamate-related scaffolding protein Homer2 influence this drug’s psychostimulant and rewarding properties. In response to neuronal activity, Homer2 is phosphorylated on S117/S216 by calcium-calmodulin kinase IIα (CaMKIIα), which induces a rapid dissociation of mGlu5-Homer2 scaffolds. Herein, we examined the requirement for Homer2 phosphorylation in cocaine-induced changes in mGlu5-Homer2 coupling, to include behavioral sensitivity to cocaine. For this, mice with alanine point mutations at (S117/216)-Homer2 (Homer2^AA/AA) were generated, and we determined their affective, cognitive and sensorimotor phenotypes, as well as cocaine-induced changes in conditioned reward and motor hyperactivity. TheHomer2^AA/AAmutation prevented activity-dependent phosphorylation of S216 Homer2 in cortical neurons, butHomer2^AA/AAmice did not differ from wild-type (WT) controls with respect to Morris maze performance, acoustic startle, spontaneous or cocaine-induced locomotion.Homer2^AA/AAmice exhibited signs of hypoanxiety similar to the phenotype of transgenic mice with a deficit in signal-regulated mGluR5 phosphorylation (Grm5^AA/AA). However, opposite ofGrm5^AA/AAmice,Homer2^AA/AAmice were less sensitive to the aversive properties of high-dose cocaine under both place-conditioning and taste-conditioning procedures. Acute injection with cocaine caused dissociation of mGluR5 and Homer2 in striatal lysates from WT, but notHomer2^AA/AAmice, suggesting a molecular basis for the deficit in cocaine aversion. These findings indicate that CaMKIIα-dependent phosphorylation of Homer2 gates the negativemore »motivational valence of high-dose cocaine via regulation of mGlu5 binding, furthering an important role for dynamic changes in mGlu5-Homer interactions in addiction vulnerability.

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