How Do People with Late Bedtimes use Social Media?

Background: The use of social media before bedtime usually results in late bedtimes, which is a prevalent cause of insufficient sleep among the general population of most countries. However, it is still unclear how people with late bedtimes use social media, which is crucial for adopting targeted behavior interventions to prevent insufficient sleep. Methods: In this study, we randomly selected 100000 users from Sina Weibo and collected all their posting through web crawling. The posting time was proposed as a proxy to identify nights on which a user stays up late. A text classifier and topic model were developed to identify the emotional states and themes of their posts. We also analyzed their posting/reposting activity, time-use patterns, and geographical distribution. Results: Our analyses show that habitually late sleepers express fewer emotions and use social media more for entertainment and getting information. People who rarely stay up late feel worse when staying up late, and they use social media more for emotional expression. People with late bedtimes mainly live in developed areas and use smartphones more when staying up late. Conclusion: This study depicts the online behavior of people with late bedtimes, which helps understand them and thereby adopt appropriately targeted interventions to avoid insufficient sleep.

Does the COVID-19 Vaccine Still Work That “Most of the Confirmed Cases Had Been Vaccinated”? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing

In July 2021, breakthrough cases were reported in the outbreak of COVID-19 in Nanjing, sparking concern and discussion about the vaccine’s effectiveness and becoming a trending topic on Sina Weibo. In order to explore public attitudes towards the COVID-19 vaccine and their emotional orientations, we collected 1542 posts under the trending topic through data mining. We set up four categories of attitudes towards COVID-19 vaccines, and used a big data analysis tool to code and manually checked the coding results to complete the content analysis. The results showed that 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine’s effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. External sources outside the vaccine might cause vaccine hesitancy. Public opinions expressed in online media reflect the public’s cognition and attitude towards vaccines and their core needs in terms of information. Therefore, online public opinion monitoring could be an essential way to understand the opinions and attitudes towards public health issues.

ILSHR Rumor Spreading Model by Combining SIHR and ILSR Models in Complex Networks

Rumor is an important form of social interaction. However, spreading harmful rumors can have a significant negative impact on social welfare. Therefore, it is important to examine rumor models. Rumors are often defined as unconfirmed details or descriptions of public things, events, or issues that are made and promoted through various tools. In this paper, the Ignorant-Lurker-Spreader-Hibernator-Removal (ILSHR) rumor spreading model has been developed by combining the ILSR and SIHR epidemic models. In addition to the characteristics of the lurker group of ILSR, this model also considers the characteristics of the hibernator group of the SIHR model. Due to the complexity of the complex network structure, the state transition function for each node is defined based on their degree to make the proposed model more efficient. Numerical simulations have been performed to compare the ILSHR rumor spreading model with other similar models on the Sina Weibo dataset. The results show more effective ILSHR performance with 95.83% accuracy than CSRT and SIR-IM models.

ТАРГЕТОВАНА РЕКЛАМА У СОЦІАЛЬНИХ МЕРЕЖАХ: ЇЇ ПОПУЛЯРНІСТЬ ТА ЕФЕКТИВНІСТЬ

Метою статті є дослідження популярності та ефективності таргетованої реклами в якості інструменту маркетингових комунікацій. Для досягнення визначеної мети використано комплекс методів: теоретичних – аналіз, синтез і систематизація наукової літератури з проблематики застосування таргетованої реклами в якості інструменту маркетингових комунікацій – для з’ясування сучасного стану дослідженості проблеми, наявності платформ для таргетингу у соціальних мережах, видів таргетованої реклами; емпіричних – бесіда, спостереження, опитування - для з’ясування основних навичок і компетенцій сучасного таргетолога, необхідних у професійній діяльності. Інформаційну базу даного наукового дослідження склали праці вітчизняних та зарубіжних науковців, статистичні дані, дані веб-аналітиків, експертів-фахівців. Доведено популярність, а також ефективність таргетованої реклами у соціальних мережах. Сформовано основні вимоги щодо навичок і компетенцій таргетолога, а саме вміння проводити маркетинговий аналіз, писати тексти, які «продають», працювати в графічних і відеоредакторах, в системах веб-аналітики. Охарактеризовано та систематизовано таргетовану рекламу за видами, зокрема до основних видів віднесено: націлювання на поведінку (націлювання на аудиторію), контекстне націлювання, пошуковий ретаргетинг, ретаргетинг сайту, прогнозне націлювання, демографічне та географічне націлювання. Проведено оцінювання ефективності таргетованої реклами у соціальних мережах та проаналізовано соціальні мережі як основні майданчики для запуску таргетованої реклами (Facebook, Instagram, Twitter, Viber, Telegram, Tik Tok, QQ, WeChat, Tumblr, Sina Weibo). Окреслено основні платформи для таргетингу у соціальних мережах – Facebook та Instagram. Наведено приклади використання таргетованої реклами роздрібною мережею Sephora, Tesco та благодійним фондом «Аманда». Наукова новизна одержаних результатів полягає у формуванні основних навичок і компетенцій сучасного таргетолога. Одержані результати дослідження можуть бути використані при підготовці фахівців-таргетологів та при організації процесу підготовки до запуску таргетованої реклами.

Evaluating Rumor Debunking Effectiveness During the COVID-19 Pandemic Crisis: Utilizing User Stance in Comments on Sina Weibo

Background: The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media.Methods: A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index (DEI) was developed to measure the effectiveness of different debunking methods.Results: The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of Car rumor and political category of Russia rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method.Conclusion: The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network’s data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors’ spatial operations are more intense than residents’ spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists’ temporal behaviors vary significantly while citizens’ movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities.

Shifting moods on Sina Weibo: The first 12 weeks of COVID-19 in Wuhan

Better understanding of social media uses in crisis situations can help improve disaster management by policy-makers, organizations, businesses, and members of the public. It can also build theoretical understanding of how social life and citizenship incorporate social media usage. This study tracks the evolution of public sentiment in Wuhan, China, during the first 12 weeks after the identification of COVID-19 on the Chinese microblogging platform Sina Weibo. Data consist of 133,079 original Sina Weibo posts dealing with the novel coronavirus. The relative prevalence of eight different emotion groups is traced longitudinally using the ROST Content Mining System and the Emotion Vocabulary of Dalian University of Technology. The study finds a progression from confusion/fear, to disappointment/frustration, to depression/anxiety, then finally to happiness/gratitude. It argues that this progression indexes the changing affective energies of digital medical citizenship, which in turn indicates the context for intervention in future crises.