Detection of spam-posting accounts on Twitter

Inuwa-Dutse, Isa; Liptrott, Mark; Korkontzelos, Ioannis

doi:10.1016/j.neucom.2018.07.044

Cited by 128 publications

(56 citation statements)

References 24 publications

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“…Research shows fake or misleading information has adverse effect in decision making process [52]. To detect fake or misleading information, existing research leverages user's profile information, user's social interaction, activity patterns and textual patterns [16]. In the context of transportation management, a future work should investigate the characteristics of fake or ambiguous tweets and how to deal with them.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter-a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment.

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Section: Discussionmentioning

confidence: 99%

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Das

Purves

2020

IEEE Trans. Intell. Transport. Syst.

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“…Similarly, Twitter data can also be used for identifying the flow of fake news [7,8,24,49]. If miss-information and unverified rumors are identified before they spread out on everyone's news feed, they can be flagged as spam or taken down.…”

Section: Social Media and Crisis Eventsmentioning

confidence: 99%

Design and analysis of a large-scale COVID-19 tweets dataset

Lamsal

2020

Appl Intell

154

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As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset's geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets' design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.

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“…User profiles and activities are the key features to detect OSD attacks (e.g, advanced spammers or crowdturfing), along with other content-based and graph-based features [82,107,108,109,199,206]. We will discuss those hybrid detection examples in Section VII-D.…”

Section: A User Profile-based Deception Detection Mechanismsmentioning

confidence: 99%

Online Social Deception and Its Countermeasures: A Survey

et al. 2021

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We are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building trustworthy SNSs. In this paper, we conduct an extensive survey, covering (i) the multidisciplinary concept of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; (iv) datasets/metrics used for validation and verification; and (v) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons learned from the existing literature. We conclude our survey with in-depth discussions on the limitations of the state-of-the-art and suggest future research directions in OSD research.

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Detection of spam-posting accounts on Twitter

Cited by 128 publications

References 24 publications

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Design and analysis of a large-scale COVID-19 tweets dataset

Online Social Deception and Its Countermeasures: A Survey

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