Microblog Entries Research Articles

Predicting social media rumours in the context of public health emergencies

The spread of rumours on social media in the context of public health emergencies often distorts perceptions of public events and obstructs crisis management. Microblog entries about 28 rumour cases are collected on Sina Weibo during the COVID-19 outbreak. The Modality–Agency–Interactivity–Navigability model is used to identify the key factors of rumour prediction. To investigate the relationship among information modality, information content, information source and rumour identification, the binary logistic regression model is established based on the features of users and microblog entries. In addition, we propose a multi-feature rumour prediction model based on the Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) models. The proposed rumour prediction model has the best performance compared with other models. The feature importance is then calculated by the SHapley Additive exPlanations (SHAP), which can also explain the XGBoost results. It is shown that the likelihood that microblog entries are rumours decreases as the values of variables such as user influence and the positive sentiment of comments rise. Microblog entries posted on Thursdays or at noon are more probably to be rumours than those posted at other time. The proposed model can assist emergency management departments in establishing a feasible rumour prediction mechanism to guide public opinion against rumours.

Tracing multisensory food experiences on Twitter

How a food, or a dish, is named and how its components and attributes are described can all influence the perception and the enjoyment of the food. Therefore, tracing patterns in food descriptions and determining their role can be of value. The aims of this study were the following: (1) to describe the multisensory food experience as represented in microblog entries concerning food and drink on Twitter, (2) to provide an overview of the changes in the above-mentioned food representations during the period 2011–20, and (3) to contribute to a broader understanding of the human–food relationship as reflected on social media – in this case Twitter – and outline its potential utility for the research field of gastrophysics. The combinations of various multisensory attributes co-occurring in a tweet (which we term ‘collocations’) found in the Twitter corpus were examined through the categories of texture, colour, taste, smell/odour, shape and sound. We mapped the collocations of the 20–25 most frequently mentioned food items and their multisensory experience pairings over time. Such time-based knowledge led to a better understanding of the multisensory experience triggers as reflected on Twitter. By analysing the multisensory experience’s frequency of occurrence, we could conclude that the category of colour is the dominant one, while textural, olfactory and auditory collocations with food are rare. In most of the cases, food tweets appear to render a food experience ‘tasty’, ‘good’ and ‘interesting’.

Prediction and Evolution of the Influence of Microblog Entries in the Context of Terrorist Events

The outbreak of terrorist events often causes tremendous damage to the country and society and arouses high attention from the public and an overwhelming response on the microblogging platform. Predicting the influence of microblogging in the context of terrorist events and revealing its evolutionary mode can help counterterrorism departments foresee potential risks, take effective countermeasures in time, and provide a reference for reducing public panic caused by terrorist events. In this study, Word2Vec is combined with the K-means clustering technique to discover the topics of microblogging, and an emotion analysis of microblogging is performed. The user features, time features, and content features of microblogging in the context of terrorist events are extracted. The prediction model of microblogging influence based on the logistic regression model was constructed and evaluated. The experimental results showed that the prediction accuracy of the model was 85.8%, which had superior performance over other six classification models. In addition, the high-influence characteristics of microblogging in the context of terrorist events were analyzed and summarized. Finally, a quantitative method of the influence of a microblogging topic based on the h-index was proposed. The evolution pattern of the influence of a microblogging topic was analyzed. The results can help predict microblog entries of high influence, understand the intensity and variation of public concern over terrorist events, and assist counterterrorism departments in taking scientific decisions.

Healthy Food Depiction on Social Media

This food computing study analyzes Twitter microblog entries related to food. A particular attention here is paid to one specific healthy food – kale. Such an approach is chosen due to kale’s popularity as a healthy food. By applying sentiment measurement, authors contribute to the understanding of text-based stories behind healthy food, and conclude that kale offers clear benefits for consumers via its specific health-related aspects (anti-inflammatory, immune system boosting, etc.) while taste is being left outside the discourse. The lack of any references to taste in kale-related social media stories leads to the conclusion that healthy food descriptions in general are short of hedonistic manifestations. Subsequently, this can be seen as one of the drawbacks of healthy food presentation textually vis-a-vis comfort foods. With this study authors add to the knowledge of the most efficient methods in shaping healthier consumer behaviors and inspire for further research of text-based information related to food.

Measuring and profiling the topical influence and sentiment contagion of public event stakeholders

When public events occur, users often generate a huge number of microblog entries and their online interactions with one another. Forwarding and commenting on posts contribute to the huge networks of topic and sentiment communication. This study constructs the topic and sentiment propagation maps of microblogging in the context of public events to visually explore the patterns of topic and sentiment propagation among stakeholders across different phases. To quantify the influence of topic and sentiment propagation, four indicators of “topic out-degree,” “topic variation degree,” “sentiment out-degree,” and “sentiment deviation degree” are proposed. We chose the child abuse case in the Beijing Red-Yellow-Blue (RYB) Kindergarten for our study. The positions of various stakeholders in the propagation paths and the relationship among stakeholders were revealed. Results indicate that the government and mainstream media have the greatest influence in terms of topic and sentiment propagation. Moreover, topic propagation was the most influential in the recession phase and the same can be said with sentiment propagation in the spreading phase. The findings can help the emergency management departments gain a better understanding of the propagation patterns of topics and emotions and the role of stakeholders in such phenomena to improve their emergency response ability.

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

AbstractThis study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

Topical evolution patterns and temporal trends of microblogs on public health emergencies

PurposeThe purpose of this paper is to identify salient topic categories and outline their evolution patterns and temporal trends in microblogs on a public health emergency across different stages. Comparisons were also examined to reveal the similarities and differences between those patterns and trends on microblog platforms of different languages and from different nations.Design/methodology/approachA total of 459,266 microblog entries about the Ebola outbreak in West Africa in 2014 on Twitter and Weibo were collected for nine months after the inception of the outbreak. Topics were detected by the latent Dirichlet allocation model and classified into several categories. The daily tweets were analyzed with the self-organizing map technique and labeled with the most salient topics. The investigated time span was divided into three stages, and the most salient topic categories were identified for each stage.FindingsIn total, 14 salient topic categories were identified in microblogs about the Ebola outbreak and were summarized as increasing, decreasing, fluctuating or ephemeral types. The topical evolution patterns of microblogs and temporal trends for topic categories vary on different microblog platforms. Twitter users were keen on the dynamics of the Ebola outbreak, such as status description, secondary events and so forth, while Weibo users focused on background knowledge of Ebola and precautions.Originality/valueThis study revealed evolution patterns and temporal trends of microblog topics on a public health emergency. The findings can help administrators of public health emergencies and microblog communities work together to better satisfy information needs and physical demands by the public when public health emergencies are in progress.

Talented Celebrity Rene Liu: Spokesperson of the Left-Over Women <em>(Sheng Nu)</em>

In the highly developed culture and entertainment market of China, the idolization of celebrities fulfils an obvious social function in terms of constructing contemporary forms of identity and community. Serving as models of persona, celebrities exert enormous influence over their fan groups. This chapter examines the classic left-over women icon created by the Taiwanese actress, singer, writer and director, Rene Liu (Liu Ruoying) —an icon created via her songs, film and television drama roles, concerts, prose writing, interviews and microblog entries. This chapter seeks to clarify how, through these many entertainment activities, Liu builds her public image and acts as a spokesperson for professional single metropolitan females. Further, this chapter discusses the controversial left-over women social phenomenon—a huge social issue involving government interference and campaign, public concern and debate in current China, which mirrors the vicissitudes and evolutions of love and marriage issues in China.

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

Provenance-Assisted Classification in Social Networks

Signal feature extraction and classification are two common tasks in the signal processing literature. This paper investigates the use of source identities as a common mechanism for enhancing the classification accuracy of social signals. We define social signals as outputs, such as microblog entries, geotags, or uploaded images, contributed by users in a social network. Many classification tasks can be defined on such outputs. For example, one may want to identify the dialect of a microblog contributed by an author, or classify information referred to in a user's tweet as true or false. While the design of such classifiers is application-specific, social signals share in common one key property: they are augmented by the explicit identity of the source. This motivates investigating whether or not knowing the source of each signal (in addition to exploiting signal features) allows the classification accuracy to be improved. We call it provenance-assisted classification. This paper answers the above question affirmatively, demonstrating how source identities can improve classification accuracy, and derives confidence bounds to quantify the accuracy of results. Evaluation is performed in two real-world contexts: (i) fact-finding that classifies microblog entries into true and false, and (ii) language classification of tweets issued by a set of possibly multi-lingual speakers. We also carry out extensive simulation experiments to further evaluate the performance of the proposed classification scheme over different problem dimensions. The results show that provenance features significantly improve classification accuracy of social signals, even when no information is known about the sources (besides their ID). This observation offers a general mechanism for enhancing classification results in social networks.

Public diplomacy meets social media: A study of the U.S. Embassy's blogs and micro-blogs

With the evolution of communication technologies, traditional public diplomacy is transforming. This study examines the practice of the U.S. Embassy's public diplomatic communication via social media, namely Chinese mainstream blogging and micro-blogging, sites using Tencent for a case study. This study analyzes the embassy's blog and micro-blog entries and an interview with the embassy's public diplomacy officer. Based on the content analysis and interview, this study discerns the key features of the U.S. Embassy's public diplomatic communication using social media and further suggests that the common values and interests related to the global public as well as experience-sharing and relationship-building might become the focus of new public diplomacy research.