Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

/pdf/social-media-and-fake-news-in-the-2016-election-1m4ttkb5mo.pdf

Social Media and Fake News in the 2016 Election

The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efﬁcacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

/pdf/detection-of-online-fake-news-using-blending-ensemble-50639u2xuf.pdf

Detection of Online Fake News Using Blending Ensemble Learning

Attention-based learning of self-media data for marketing intention detection

People’s daily information sharing and acquisition through the Internet has become more and more popular. The comprehensive multimodal marketing advertorial generated by ‘We Media’ accounts besides the normal social news is gaining its importance on social media platforms. In order to achieve effective advertising, the marketing intent understanding is a key step towards generating targeted advertising strategies (push advertorials to specific people at a specific time). However, advertorials in real are usually designed to pretend as normal social news with a wide range of contents. This poses big challenges to the platforms on accurately recognizing and analyzing the marketing intents behind the advertorials. As a pioneering study, we address this new problem of multimodalbased marketing intent analysis and answer three core questions: (1) does a piece of social news contain marketing intent? (2) what is the topic of marketing intent? (3) what is the extent of marketing intent? Towards this end, we propose a novel Multimodal-based Marketing Intent Analysis scheme (MMIA) to estimate the marketing intent embedded in the multimodal contents. Specifically, a novel supervised neural autoregressive model (SmiDocNADE) is proposed to enhance the discriminative capacity of the learned hidden features so that a single system is capable of solving the three questions. In order to effectively model inter-correlations between images and text in advertorials, we fuse multimodal data and extract features by Graph Convolution Networks as an enhancement to SmiDocNADE. The extensive evaluations demonstrate the advantages of our proposed system in multimodal-based marketing intent analysis from multiple aspects.

/pdf/multimodal-marketing-intent-analysis-for-effective-targeted-5cm4gzl06o.pdf

Multimodal Marketing Intent Analysis for Effective Targeted Advertising

Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors’ opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term “opportunities” that are akin to “anticipation” in Plutchik’s theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors’ decision making.

/pdf/detection-of-financial-opportunities-in-micro-blogging-data-2mal5y153m.pdf

Detection of Financial Opportunities in Micro-Blogging Data With a Stacked Classification System

Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.

https://www.mdpi.com/1999-5903/11/7/155/pdf

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

With the age of incoming of self-media, everyone can be the author of the content in the media age of big data. This has caused a mass of fake news appearing in the network. Authors of these fake news will mislead the public by spreading and it will bring economic and social benefits. Existing work focuses on using the various types of features of the article in the hope that a way to accurately identify fake news can be found, but this undermines their universality. In this paper, we propose a pipeline that combines preprocessing, feature extraction and model fusion for a more accurate and automated prediction. Specially we fusion of latent semantic analysis (LSA) and ensemble learning model results using stacking. Experimental analysis of real-world data demonstrates that our pipeline achieves higher accuracy than existing approaches.

Stacking-Based Ensemble Learning on Low Dimensional Features for Fake News Detection

Intelligent vehicles require both communications and radar sensing. Recently, the development of joint communication and automotive radar sensing has attracted great attention. In this paper, the reference signal (RS) in the orthogonal frequency division multiplexing (OFDM) waveform is leveraged for radar sensing in 5G vehicle-to-everything (V2X) communications. Unlike the existing studies that use the whole subcarriers as pilot subcarriers for radar sensing, the scattered RS pattern adopted in the 5G system is considered in our design. More specifically, the scattered RS placement and power allocation between RS and data are optimized to minimize the total transmission power for energy-efficient joint signal transmission, while satisfying both the communication and radar sensing requirements. The optimization problem is a mixed-integer non-linear programming (MINLP) problem. The optimal solution is solved by enumerating all possible RS placements with the optimal allocated power by solving a transformed convex optimization problem. Such an approach suffers a high computing complexity when there are a large number of possible RS placements. Therefore, an alternative heuristic approach is further developed to resolve the problem via successive convex approximation (SCA) method. Simulation results show that the heuristic method can well approach the optimal solution. Compared with existing studies, our proposed scheme can effectively improve energy efficiency.

Energy-Efficient Reference Signal Optimization for 5G V2X Joint Communication and Sensing

Backscatter communication is visioned as one of the promising technologies for future ultra-low power Internet of Things (IoT). The orthogonal-frequency-division-multiplexing physical-layer (OFDM-PHY) WiFi backscatter communications attract great attention in recent years. However, the severe tag transmission collisions highly degrade the system performance in a backscatter network, since complicated multiple access mechanisms, e.g., carrier sense multiple access, cannot be applied on backscatter tags. To address this problem, a novel parallel decoding design called FreeCollision is developed to enable concurrent OFDM-PHY WiFi backscatter communications. Without the prior knowledge of the number of collided tags, their channel state information, and modulation types, FreeCollsion can just use the I-Q symbols to resolve the collision by the design of a series of mechanisms: collided constellation recovery, concurrent virtual channel estimation, QPSK tag detection, and parallel demodulation. Simulation results verify the effectiveness of our proposed scheme. The successful decoding rate is more than 95% for 4 collided tags with BPSK modulation. The maximum successful transmission probability can be improved from 36.7% to 88% for slotted Aloha.

Songqian Li

Papers

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

Stacking-Based Ensemble Learning on Low Dimensional Features for Fake News Detection

Energy-Efficient Reference Signal Optimization for 5G V2X Joint Communication and Sensing

FreeCollision: Parallel Decoding for Concurrent OFDM-PHY WiFi Backscatter Communications