scholarly journals A novel few-shot learning based multi-modality fusion model for COVID-19 rumor detection from online social media

2021 ◽  
Vol 7 ◽  
pp. e688
Author(s):  
Heng-yang Lu ◽  
Chenyou Fan ◽  
Xiaoning Song ◽  
Wei Fang
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Early Stage ◽  
Fusion Model ◽  
Daily Lives ◽  
Sina Weibo ◽  
Detection Techniques ◽  
Online Social Media ◽  
Meta Learning ◽  
Rumor Detection

Background Rumor detection is a popular research topic in natural language processing and data mining. Since the outbreak of COVID-19, related rumors have been widely posted and spread on online social media, which have seriously affected people’s daily lives, national economy, social stability, etc. It is both theoretically and practically essential to detect and refute COVID-19 rumors fast and effectively. As COVID-19 was an emergent event that was outbreaking drastically, the related rumor instances were very scarce and distinct at its early stage. This makes the detection task a typical few-shot learning problem. However, traditional rumor detection techniques focused on detecting existed events with enough training instances, so that they fail to detect emergent events such as COVID-19. Therefore, developing a new few-shot rumor detection framework has become critical and emergent to prevent outbreaking rumors at early stages. Methods This article focuses on few-shot rumor detection, especially for detecting COVID-19 rumors from Sina Weibo with only a minimal number of labeled instances. We contribute a Sina Weibo COVID-19 rumor dataset for few-shot rumor detection and propose a few-shot learning-based multi-modality fusion model for few-shot rumor detection. A full microblog consists of the source post and corresponding comments, which are considered as two modalities and fused with the meta-learning methods. Results Experiments of few-shot rumor detection on the collected Weibo dataset and the PHEME public dataset have shown significant improvement and generality of the proposed model.

A Literature Review on the Viral Advertising Narrative Structure

2019 ◽  
pp. 1363-1379
Author(s):  
Murat Koçyiğit
Keyword(s):  
Social Media ◽  
Early Stage ◽  
Narrative Structure ◽  
Viral Marketing ◽  
Stage Of Development ◽  
Online Social Media ◽  
Central Platform ◽  
Social Messages ◽  
Advertising Content ◽  
Viral Advertising

Viral advertising relies on consumers' transmitting the message to other consumers within their online social media. Viral advertising is controlled by consumers and is less under the control of advertisers and brands (Petrescu, 2014). Consumers receive the link or the advertising content and pass it along through e-mail or posting it on a blog, microblog, podcast, wiki, form, webpage, and social media profile. Advertising narrative in traditional media has changed with viral ads. In the narrative of viral advertising is more emotional, romantic, humorous, sexual and contains social messages. This study was conducted to examine the Brands' viral advertising narrative. Viral advertising is at an early stage of development and much of the current viral marketing communication literature research is concerned with understanding the motivations and behaviours of those passing-on email messages. No longer the preserve of offline communication strategists, it is becoming a central platform for interactive marketing communications (Cruz & Fill, 2008).

Data retrieval from online social media networks for defining business angels’ profile

2019 ◽  
Vol 14 (1) ◽  
pp. 57-75
Author(s):  
Gustavo Morales-Alonso ◽  
Guzmán A. Vila ◽  
Isaac Lemus-Aguilar ◽  
Antonio Hidalgo
Keyword(s):  
Social Media ◽  
Early Stage ◽  
Data Retrieval ◽  
Business Angels ◽  
Informal Finance ◽  
Content Type ◽  
Highly Educated ◽  
Social Media Networks ◽  
Retrieval Technique ◽  
Online Social Media

Purpose Entrepreneurship is the basis of economic development but is somehow limited by the lack of access to financing sources, especially in the crucial moments of start-up early-stage development. For crossing the so-called “valley of death,” start-ups need to access informal finance sources, such as business angels. This study aims at defining the profile of business angels and comparing it with the existing literature. Design/methodology/approach A novel methodology for sampling the business angles population has been used, which extracts data from online social media networks. This allows taking a closer look at informal sources of entrepreneurial finance. A total of 500 real business angels, acting worldwide, from the LinkedIn and Crunchbase databases has been retrieved for this study. Findings Results point out that younger investors seem to be entering the entrepreneurial informal finance market. They are mainly males between 40 and 50 years of age, with a previous entrepreneurial record, and more highly educated than previously stated. They tend to have studies from Business Administration and Economics, although they prefer to invest in the ICT sector. Originality/value Besides the novel data retrieval technique for analyzing the informal sources of finance, the originality of the work lies in updating the archetype for business angels.

scholarly journals Social Media Rumor Refuter Feature Analysis and Crowd Identification Based on XGBoost and NLP

2020 ◽  
Vol 10 (14) ◽  
pp. 4711 ◽  
Author(s):  
Zongmin Li ◽  
Qi Zhang ◽  
Yuhong Wang ◽  
Shihang Wang
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Classification Model ◽  
Dark Side ◽  
Feature Analysis ◽  
Online Information ◽  
Sina Weibo ◽  
Machine Learning Methods ◽  
Model Training ◽  
Shed Light

One prominent dark side of online information behavior is the spreading of rumors. The feature analysis and crowd identification of social media rumor refuters based on machine learning methods can shed light on the rumor refutation process. This paper analyzed the association between user features and rumor refuting behavior in five main rumor categories: economics, society, disaster, politics, and military. Natural language processing (NLP) techniques are applied to quantify the user’s sentiment tendency and recent interests. Then, those results were combined with other personalized features to train an XGBoost classification model, and potential refuters can be identified. Information from 58,807 Sina Weibo users (including their 646,877 microblogs) for the five anti-rumor microblog categories was collected for model training and feature analysis. The results revealed that there were significant differences between rumor stiflers and refuters, as well as between refuters for different categories. Refuters tended to be more active on social media and a large proportion of them gathered in more developed regions. Tweeting history was a vital reference as well, and refuters showed higher interest in topics related with the rumor refuting message. Meanwhile, features such as gender, age, user labels and sentiment tendency also varied between refuters considering categories.

scholarly journals Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

2020 ◽  
Vol 9 (6) ◽  
pp. 402 ◽  
Author(s):  
Zhenghong Peng ◽  
Ru Wang ◽  
Lingbo Liu ◽  
Hao Wu
Keyword(s):  
Public Health ◽  
Social Media ◽  
Urban Space ◽  
Elderly Population ◽  
Early Stage ◽  
The Elderly ◽  
Preventive Strategy ◽  
Sina Weibo ◽  
Spatial Features ◽  
Distribution Features

During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.

Rumor Detection on Twitter Using a Supervised Machine Learning Framework

2021 ◽  
pp. 452-465
Author(s):  
Hardeo Kumar Thakur ◽  
Anand Gupta ◽  
Ayushi Bhardwaj ◽  
Devanshi Verma
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Supervised Machine Learning ◽  
Learning Framework ◽  
Online Social Media ◽  
Rumor Propagation ◽  
Multiple Classification ◽  
The Government ◽  
Rumor Detection

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work, analyzing temporal and linguistic characteristics of rumors seems to give ample time for rumor propagation. Meanwhile, with the huge outburst of data on social media, studying these characteristics for each tweet becomes spatially complex. Therefore, in this article, a two-fold supervised machine-learning framework is proposed that detects rumors by filtering and then analyzing their linguistic properties. This method attempts to automate filtering by training multiple classification algorithms with accuracy higher than 81.079%. Finally, using textual characteristics on the filtered data, rumors are detected. The effectiveness of the proposed framework is shown through extensive experiments on over 10,000 tweets.

Event news detection and citizens community structure for disaster management in social networks

2019 ◽  
Vol 43 (1) ◽  
pp. 113-132 ◽  
Author(s):  
Radhia Toujani ◽  
Jalel Akaichi
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Event Detection ◽  
Fuzzy Theory ◽  
Entity Recognition ◽  
News Story ◽  
Clustering Method ◽  
Content Type ◽  
Detection Techniques ◽  
Clustering Quality

Purpose Nowadays, the event detection is so important in gathering news from social media. Indeed, it is widely employed by journalists to generate early alerts of reported stories. In order to incorporate available data on social media into a news story, journalists must manually process, compile and verify the news content within a very short time span. Despite its utility and importance, this process is time-consuming and labor-intensive for media organizations. Because of the afore-mentioned reason and as social media provides an essential source of data used as a support for professional journalists, the purpose of this paper is to propose the citizen clustering technique which allows the community of journalists and media professionals to document news during crises. Design/methodology/approach The authors develop, in this study, an approach for natural hazard events news detection and danger citizen’ groups clustering based on three major steps. In the first stage, the authors present a pipeline of several natural language processing tasks: event trigger detection, applied to recuperate potential event triggers; named entity recognition, used for the detection and recognition of event participants related to the extracted event triggers; and, ultimately, a dependency analysis between all the extracted data. Analyzing the ambiguity and the vagueness of similarity of news plays a key role in event detection. This issue was ignored in traditional event detection techniques. To this end, in the second step of our approach, the authors apply fuzzy sets techniques on these extracted events to enhance the clustering quality and remove the vagueness of the extracted information. Then, the defined degree of citizens’ danger is injected as input to the introduced citizens clustering method in order to detect citizens’ communities with close disaster degrees. Findings Empirical results indicate that homogeneous and compact citizen’ clusters can be detected using the suggested event detection method. It can also be observed that event news can be analyzed efficiently using the fuzzy theory. In addition, the proposed visualization process plays a crucial role in data journalism, as it is used to analyze event news, as well as in the final presentation of detected danger citizens’ clusters. Originality/value The introduced citizens clustering method is profitable for journalists and editors to better judge the veracity of social media content, navigate the overwhelming, identify eyewitnesses and contextualize the event. The empirical analysis results illustrate the efficiency of the developed method for both real and artificial networks.

scholarly journals MO’ CHARACTERS MO’ PROBLEMS: ONLINE SOCIAL MEDIA PLATFORM CONSTRAINTS AND MODES OF COMMUNICATION

2020 ◽  
Author(s):  
Lewis Mitchell ◽  
Joshua Dent ◽  
Joshua Ross
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
English Language ◽  
Length Change ◽  
Reading Level ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Media Platform ◽  
Processing Techniques

It is widely accepted that different online social media platforms produce different modes of communication, however the ways in which these modalities are shaped by the constraints of a particular platform remain difficult to quantify. On 7 November 2017 Twitter doubled the character limit for users to 280 characters, presenting a unique opportunity to study the response of this population to an exogenous change to the communication medium. Here we analyse a large dataset comprising 387 million English-language tweets (10% of all public tweets) collected over the September 2017--January 2018 period to quantify and explain large-scale changes in individual behaviour and communication patterns precipitated by the character-length change. Using statistical and natural language processing techniques we find that linguistic complexity increased after the change, with individuals writing at a significantly higher reading level. However, we find that some textual properties such as statistical language distribution remain invariant across the change, and are no different to writings in different online media. By fitting a generative mathematical model to the data we find a surprisingly slow response of the Twitter population to this exogenous change, with a substantial number of users taking a number of weeks to adjust to the new medium. In the talk we describe the model and Bayesian parameter estimation techniques used to make these inferences. Furthermore, we argue for mathematical models as an alternative exploratory methodology for "Big" social media datasets, empowering the researcher to make inferences about the human behavioural processes which underlie large-scale patterns and trends.

Hooligan Sparrow: Representation of sexual assault in Chinese cinema, feminist activism and the limits of #MeToo in China

Asian Cinema ◽  
2021 ◽  
Vol 32 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Mirela David
Keyword(s):  
Social Media ◽  
Sexual Assault ◽  
Sexual Violence ◽  
Power Dynamics ◽  
Chinese Cinema ◽  
Feminist Activism ◽  
Sina Weibo ◽  
Female Directors ◽  
Online Social Media ◽  
Female Director

Hooligan Sparrow breaks with many taboos in Chinese cinema. It is the first internationally acclaimed documentary by a Chinese female director to centre upon investigating the activities of Ye Haiyan, a Chinese sex and women’s rights activist, as well as to address the politically sensitive topic of sexual assault in China. This is the first study to examine the cinematic contributions of Wang Nanfu and Ye Haiyan’s activism and feminist writings posted on Ye’s online social media accounts on Sina Weibo and Twitter. I unpack the power dynamics in this documentary as well as the interplay between the filmmaker’s subjectivity and the female rights activist’s subjectivity. This study also investigates how masculine aesthetic representations of sexual assault in Chinese cinema have blurred the issue of consent and shows how the subjectivities of female directors like Wang Nanfu and Vivian Qu bring more impactful representations of sexual violence in Chinese cinema.

A Literature Review on the Viral Advertising Narrative Structure

2017 ◽  
pp. 167-183 ◽  
Author(s):  
Murat Koçyiğit
Keyword(s):  
Social Media ◽  
Early Stage ◽  
Narrative Structure ◽  
Viral Marketing ◽  
Stage Of Development ◽  
Online Social Media ◽  
Central Platform ◽  
Social Messages ◽  
Advertising Content ◽  
Viral Advertising

Viral advertising relies on consumers' transmitting the message to other consumers within their online social media. Viral advertising is controlled by consumers and is less under the control of advertisers and brands (Petrescu, 2014). Consumers receive the link or the advertising content and pass it along through e-mail or posting it on a blog, microblog, podcast, wiki, form, webpage, and social media profile. Advertising narrative in traditional media has changed with viral ads. In the narrative of viral advertising is more emotional, romantic, humorous, sexual and contains social messages. This study was conducted to examine the Brands' viral advertising narrative. Viral advertising is at an early stage of development and much of the current viral marketing communication literature research is concerned with understanding the motivations and behaviours of those passing-on email messages. No longer the preserve of offline communication strategists, it is becoming a central platform for interactive marketing communications (Cruz & Fill, 2008).

Disinformation and misinformation triangle

2019 ◽  
Vol 75 (5) ◽  
pp. 1013-1034 ◽  
Author(s):  
Victoria L. Rubin
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Information Science ◽  
Interdisciplinary Collaboration ◽  
Literacy Skills ◽  
Causal Factors ◽  
Content Type ◽  
Detection Techniques ◽  
Digital News ◽  
Credible Information

Purpose The purpose of this paper is to treat disinformation and misinformation (intentionally deceptive and unintentionally inaccurate misleading information, respectively) as a socio-cultural technology-enabled epidemic in digital news, propagated via social media. Design/methodology/approach The proposed disinformation and misinformation triangle is a conceptual model that identifies the three minimal causal factors occurring simultaneously to facilitate the spread of the epidemic at the societal level. Findings Following the epidemiological disease triangle model, the three interacting causal factors are translated into the digital news context: the virulent pathogens are falsifications, clickbait, satirical “fakes” and other deceptive or misleading news content; the susceptible hosts are information-overloaded, time-pressed news readers lacking media literacy skills; and the conducive environments are polluted poorly regulated social media platforms that propagate and encourage the spread of various “fakes.” Originality/value The three types of interventions – automation, education and regulation – are proposed as a set of holistic measures to reveal, and potentially control, predict and prevent further proliferation of the epidemic. Partial automated solutions with natural language processing, machine learning and various automated detection techniques are currently available, as exemplified here briefly. Automated solutions assist (but not replace) human judgments about whether news is truthful and credible. Information literacy efforts require further in-depth understanding of the phenomenon and interdisciplinary collaboration outside of the traditional library and information science, incorporating media studies, journalism, interpersonal psychology and communication perspectives.

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