scholarly journals Model-oriented fake news detection on social media

2019 ◽  
pp. 31-36
Author(s):  
M.O. Давіденко ◽  
T.O. Білобородова
Keyword(s):  
Social Media ◽  
Feature Extraction ◽  
Rand Index ◽  
Training Data ◽  
Adjusted Rand Index ◽  
Fake News ◽  
Silhouette Coefficient ◽  
The Social ◽  
Text Feature ◽  
Oriented Approach

Nowadays, fake news (FN) have actively penetrated throughout the social media reducing our ability to critical assess and proceed the information. Most of existing approaches to handle with FN require a labeled FN training datasets but in some cases these datasets are unavailable. In this paper, we present a model-oriented approach for FN detection and feature extraction. The unsupervised technique for FN identification without the training data is designed and developed. It includes four main steps, namely data preprocessing, text feature extraction, vectorization, and clustering using k-means algorithm. The results of the last step was evaluated through several parameters: homogeneity, completeness, V-measure, Adjusted Rand index and Silhouette coefficient.  

scholarly journals Fake News Detection Models and Performances

2019 ◽  
Vol 9 (2) ◽  
pp. 3754-3757
Keyword(s):  
Social Media ◽  
Feature Extraction ◽  
Language Processing ◽  
Fake News ◽  
Inaccurate Information ◽  
Document Frequency ◽  
Information Time ◽  
The Social ◽  
Text Feature ◽  
Important Approach

Fake News detection is a hard problem for decades after the advent of social media. As misinformation, so called fake news continues to be rapidly distributing on internet, the reality has becoming increasingly shaped by false information. Time after time we have consumed or being exposed to inaccurate information. The last few years have been talking about guarding against misinformation and not progressed much in this direction. The social media is one of the medium where the fake news spreads so rapidly and impact many in a lesser span of time. Machine Learning and Natural Language processing are the core techniques to detect the fake news and stopping from spreading on social media. Many researchers putting their effort in this new challenge to curb down. This paper provides an insight on feature extraction techniques used for fake news detection on soft media. Text feature extraction works with extracting the document information which represent the whole document without loss of the sole information but words which are considered irrelevant were ignored for the purpose of improving the accuracy. Term Frequency Inverse Document Frequency (TF-IDF), BoW(Bag of Words) are some of the important techniques used in text feature extraction. These techniques are discussed with their significance in this paper. One of the important approach, Automated Readability Index is used to test the readability of the text to build the model also discussed in this paper. This paper will play a significant role for the researchers who are interested in the area of fake news Identification.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

scholarly journals Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 248
Author(s):  
Simone Leonardi ◽  
Giuseppe Rizzo ◽  
Maurizio Morisio
Keyword(s):  
Social Media ◽  
Network Architecture ◽  
Diffusion Mechanism ◽  
Automated Classification ◽  
Fake News ◽  
Neural Network Architecture ◽  
User Classification ◽  
The Social ◽  
Network Topologies

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

scholarly journals Loodus tuleb tagasi: delfiinid ja saurused. Libauudistest, photoshop-muistenditest ja meemidest koroonakarantiini ajal

Mäetagused ◽  
2021 ◽  
Vol 79 ◽  
pp. 167-184
Author(s):  
Eda Kalmre ◽  
Keyword(s):  
Social Media ◽  
Coping Strategies ◽  
Well Being ◽  
Point Of View ◽  
Human Existence ◽  
The Other ◽  
Fake News ◽  
The Social ◽  
Truth Value ◽  
Positive Effect

The article follows the narrative trend initiated by the social media posts and fake news during the first months of the corona quarantine, which claims that the decrease of contamination due to the quarantine has a positive effect on the environment and nature recovery. The author describes the context of the topic and follows the changes in the rhetoric through different genres, discussing the ways in which a picture can tell a truthful story. What is the relation between the context, truth, and rhetoric? This material spread globally, yet it was also readily “translated” into the Estonian context, and – what is very characteristic of the entire pandemic material – when approaching this material, truthful and fabricated texts, photos, and videos were combined. From the folkloristic point of view, these rumours in the form of fake news, first presented in the function of a tall tale and further following the sliding truth scale of legends, constitute a part of coping strategies, so-called crisis humour, yet, on the other hand, also a belief story presenting positive imagery, which surrounds the mainly apocalyptically perceived pandemic period and interprets the human existence on a wider scale. Even if these fake news and memes have no truth value, they communicate an idea – nature recovers – and definitely offer hope and a feeling of well-being.

scholarly journals Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 61-64
Author(s):  
Mohammad Zoqi Sarwani ◽  
Dian Ahkam Sani
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Probabilistic Neural Network ◽  
Media Analysis ◽  
Training Data ◽  
The Internet ◽  
Accuracy Score ◽  
Network Algorithm ◽  
The Social ◽  
Neural Network Algorithm

The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.

Digital Disinformation and the Imaginative Dimension of Communication

2020 ◽  
Vol 97 (2) ◽  
pp. 435-452
Author(s):  
Jason Vincent A. Cabañes
Keyword(s):  
Social Media ◽  
Fake News ◽  
Political Landscape ◽  
Media Manipulation ◽  
Shared Understandings ◽  
Oriented Approach

To nuance current understandings of the proliferation of digital disinformation, this article seeks to develop an approach that emphasizes the imaginative dimension of this communication phenomenon. Anchored on ideas about the sociality of communication, this piece conceptualizes how fake news and political trolling online work in relation to particular shared understandings people have of their socio-political landscape. It offers the possibility of expanding the information-oriented approach to communication taken by many journalistic interventions against digital disinformation. It particularly opens up alternatives to the problematic strategy of challenging social media manipulation solely by doubling down on objectivity and facts.

scholarly journals Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task

2018 ◽  
Vol 25 (10) ◽  
pp. 1274-1283 ◽  
Author(s):  
Abeed Sarker ◽  
Maksim Belousov ◽  
Jasper Friedrichs ◽  
Kai Hakala ◽  
Svetlana Kiritchenko ◽  
...  
Keyword(s):  
Social Media ◽  
Text Classification ◽  
Training Data ◽  
Support Vector ◽  
Shared Task ◽  
Social Media Mining ◽  
The Social ◽  
Related Text ◽  
Health Related ◽  
Media Mining

AbstractObjectiveWe executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.Materials and MethodsWe organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks.ResultsAmong 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems.DiscussionAmong individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1).ConclusionsData imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1).

scholarly journals Reviewing Sentiment Analysis at the Shallow End

2020 ◽  
Vol 8 (4) ◽  
pp. 47-62
Author(s):  
Francisca Oladipo ◽  
Ogunsanya, F. B ◽  
Musa, A. E. ◽  
Ogbuju, E. E ◽  
Ariwa, E.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
Training Data ◽  
Data Set ◽  
The Social ◽  
Machine Learning Approach ◽  
Media Space ◽  
Social Media Platforms

The social media space has evolved into a large labyrinth of information exchange platform and due to the growth in the adoption of different social media platforms, there has been an increasing wave of interests in sentiment analysis as a paradigm for the mining and analysis of users’ opinions and sentiments based on their posts. In this paper, we present a review of contextual sentiment analysis on social media entries with a specific focus on Twitter. The sentimental analysis consists of two broad approaches which are machine learning which uses classification techniques to classify text and is further categorized into supervised learning and unsupervised learning; and the lexicon-based approach which uses a dictionary without using any test or training data set, unlike the machine learning approach.  

scholarly journals Spreading Fake News in the Virtual Realm in Bangladesh: Assessment of Impact

2020 ◽  
pp. 11-25
Author(s):  
Sharifa Umma Shirina ◽  
Md. Tabiur Rahman Prodhan
Keyword(s):  
Social Media ◽  
Social Impact ◽  
National Election ◽  
Technological Advancement ◽  
Fake News ◽  
Supersonic Speed ◽  
News Reporting ◽  
The Social ◽  
The World ◽  
Acute Crisis

Fake news is ‘false, often sensational, information disseminated under the guise of news reporting.’ The upsurge of technological advancement, especially social media, has paved the way for spreading fake news. The virtual realm spurs fake news as per the speed of air. Nowadays, fake news has been one of the social problems in the world along with Bangladesh. Self-seeker groups use fake news as an ‘atomic arsenal’ to disseminate their popular rhetoric with supersonic speed for fulfilling male purposes. Fake news is usually rampant during any crisis, elections, and even in campaigns. The hoaxers and fakers exploit the opportunity of the wavering psychology of the social media users, and fake news becomes ‘viral’ on social media, Facebook. Recently Bangladesh has faced an acute crisis of spreading fake news during the ‘Movement of Nirapod Sarak Chai, ‘National election in December 2018’ and very recent ‘need child’s head for Padma Bridge.’ This study titled “Spreading Fake News in the Virtual Realm in Bangladesh: Assessment of Impact” seeks the reasons for spreading fake news and its’ social impact in Bangladesh.

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