Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

Fake News Detection on Social Media: A Data Mining Perspective

Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of "fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or the arguments in its content. Through a unique study of three data sets and features that capture the style and the language of articles, we show that this assumption is not true. Fake news in most cases is more similar to satire than to real news, leading us to conclude that persuasion in fake news is achieved through heuristics rather than the strength of arguments. We show overall title structure and the use of proper nouns in titles are very significant in differentiating fake from real. This leads us to conclude that fake news is targeted for audiences who are not likely to read beyond titles and is aimed at creating mental associations between entities and claims.

This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News

A survey on fake news and rumour detection techniques

The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention. This survey comprehensively and systematically reviews fake news research. The survey identifies and specifies fundamental theories across various disciplines, e.g., psychology and social science, to facilitate and enhance the interdisciplinary research of fake news. Current fake news research is reviewed, summarized and evaluated. These studies focus on fake news from four perspective: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its creators and spreaders. We characterize each perspective with various analyzable and utilizable information provided by news and its spreaders, various strategies and frameworks that are adaptable, and techniques that are applicable. By reviewing the characteristics of fake news and open issues in fake news studies, we highlight some potential research tasks at the end of this survey.

Fake News: A Survey of Research, Detection Methods, and Opportunities.

Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link Such headlines are known as Clickbaits While these baits may trick the readers into clicking, in the long-run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines The extension also offers each reader an option to block clickbaits she doesn't want to see Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits

Stop clickbait: detecting and preventing clickbaits in online news media

Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. 
In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.

Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media

In this work, we give a practical implementation of the well known impossible differential attack on 5 round AES-128 given by Biham and Keller. The complexity of the original attack is in the order of the practical realm with time complexity \(2^{31}\) and data complexity \(2^{29.5}\). However, the primary memory required to execute the attack was 4 TB making it difficult to implement which is supported by the fact that there are no reported implementations of the attack. We propose a data-memory tradeoff for the attack which lets us reduce memory needed at the expense of increased data complexity. We have been able to implement the attack using 128.5 GB of primary memory and \(2^{32}\) data complexity. Though the data complexity is increased by about 4.65 times, it makes up for the fact that we decreased the memory usage by about 32 times. We also extend the implementation to 5 round AES-192/256. To the best of our knowledge, the implementations of attacks in this work are the first ones available publicly.

On the Practical Implementation of Impossible Differential Cryptanalysis on Reduced-Round AES

This work presents practical key-recovery attacks on round-reduced variants of CAESAR Round 2 candidate PAEQ by analyzing it in the light of guess-and-determine analysis. The attack developed here targets the mode of operation along with diffusion inside the AES based internal permutation AESQ. The first attack uses a guess-and-invert technique leading to a meet-in-the-middle attack that is able to recover the key for 6 out of the 20 rounds of paeq-64/80/128 with reduced key entropy of \(1,2^{16}\) and \(2^{32}\) respectively. The second analysis extends the attack to 7 rounds using a invert-and-guess strategy which results in reduced key-space of \(2^{24},2^{32}\) and \(2^{40}\) for the same PAEQ variants. Finally, an 8-round attack is mounted using a guess-invert-guess strategy which works on any of the three variants with a complexity of \(2^{48}\). Moreover, unlike the CICO attack mounted by the designers which works with only AESQ, our 8-round attack additionally takes into account the mode of operation of PAEQ.

Gain : Practical Key-Recovery Attacks on Round-Reduced PAEQ

The authors explore a cryptanalysis strategy which seems to be particularly applicable to parallelisable ciphers where the key forms a part of the internal state. The proposed technique combines internal differentials with guess and determine analysis to come up with what is referred to as the match-in-the-end attack. The notion of difference here deviates from the classical differential where the difference is controllable via the plaintext/ciphertext. Here, they exploit the Hamming distance between parallel branches to devise the differential trail. They apply the strategy on full eight (out of 20) rounds of parallelisable authenticated cipher [parallelisable AE based on quadrupled AES (PAEQ)] to devise key recovery attacks with practical time complexities. They first show an initial attack on paeq-64/80/128 and then devise improvements which give us the best keyrecovery attacks with time complexities of 2
 33
, 2
 48
, and 2
 64
, respectively. While the best reported attacks on eight-round paeq-64/80/128 have a data complexity of 289 blocks, the result improves their time complexities by factors of 2, 2
 18
, and 2
 34
, while preserving the data complexity. Finally, they present a nonce-based differential attack which works on paeq-128-t with 2
 64
 time complexity but uses just two single block known plaintexts making it the most practical attack on any round-reduced PAEQ variant reported so far.

Sourya Kakarla

Papers

Stop clickbait: detecting and preventing clickbaits in online news media

Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media

On the Practical Implementation of Impossible Differential Cryptanalysis on Reduced-Round AES

Gain : Practical Key-Recovery Attacks on Round-Reduced PAEQ

Dinamite: internal differential match-in-the-end attack on eight-round PAEQ