A holistic system for troll detection on Twitter

Fornacciari, Paolo; Mordonini, Monica; Poggi, Agostino; Sani, Laura; Tomaiuolo, Michele

doi:10.1016/j.chb.2018.08.008

Cited by 65 publications

(44 citation statements)

References 18 publications

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“…Online influence campaign detection offers a useful example where this problem is evident. The results of this work offer some future direction for the leveraging of content-based features for influence account detection, which may be integrated into an ensemble model for influence campaign detection on Reddit, similar to recent work in building ensemble troll detection models on Twitter [18]. Synthesizing these features with past work on ensemble approaches to influence campaign detection on Reddit [19] may contribute to an ethically sound and effective approach.…”

Section: Methodsmentioning

confidence: 63%

Towards Ethical Content-Based Detection Of Online Influence Campaigns

Crothers

Japkowicz

Viktor

2019

2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

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The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development. We demonstrate that features derived from the text of user comments are useful for identifying suspect activity, but lead to increased erroneous identifications (false positive classifications) when keywords over-represented in past influence campaigns are present. Drawing on research in native language identification (NLI), we use "named entity masking" (NEM) to create sentence features robust to this shortcoming, while maintaining comparable classification accuracy. We demonstrate that while NEM consistently reduces false positives when key named entities are mentioned, both masked and unmasked models exhibit increased false positive rates on English sentences by Russian native speakers, raising ethical considerations that should be addressed in future research.Index Terms-influence campaign detection, native language identification, algorithmic bias, natural language processing, bidirectional encoder representations from transformers (BERT).

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Section: Methodsmentioning

confidence: 63%

Towards Ethical Content-Based Detection Of Online Influence Campaigns

Crothers

Japkowicz

Viktor

2019

2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

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“…It is rather the result of a top-down incremental browsing of the literature, starting from seminal papers and iteratively following new paths according to the suggestions and discoveries of the newly accessed ones. Substantially, it reflects and expands the strictly application-oriented analysis of the state-of-the-art we performed in the first stage of development of [14].…”

Section: Introductionmentioning

confidence: 77%

“…Our future work will start from the results of the works presented in this survey and from a tool for data analysis that we have recently developed and tested [42]. Our first experimentations were related to the Twitter communities and our future work will be dedicated to improving the solutions presented in [14] and to experiment the tool in other types of communities.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Troll Detection

et al. 2020

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A troll is usually defined as somebody who provokes and offends people to make them angry, who wants to dominate any discussion or who tries to manipulate people's opinions. The problems caused by such persons have increased with the diffusion of social media. Therefore, on the one hand, press bodies and magazines have begun to address the issue and to write articles about the phenomenon and its related problems while, on the other hand, universities and research centres have begun to study the features characterizing trolls and to look for solutions for their identification. This survey aims at introducing the main researches dedicated to the description of trolls and to the study and experimentation of methods for their detection.

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“…A promising approach is the automatic detection of trolls, which may be used for excluding abusing users or at least to advise them to moderate their own mood and behaviours. Various automatic classifiers are described in the scientific literature, adopting different approaches and analyzing different user features: polarity of messages [43], frequency of generated answers [44], reliability and reputation [45], user profiles [46], or even the combination of multiple kinds of analyses, in a holistic approach [47,48].…”

Section: Drawbacks and Obstacles To Participationmentioning

confidence: 99%

Adoption of Social Media in Socio-Technical Systems: A Survey

2021

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This article describes the current landscape in the fields of social media and socio-technical systems. In particular, it analyzes the different ways in which social media are adopted in organizations, workplaces, educational and smart environments. One interesting aspect of this integration, is the use of social media for members’ participation and access to the processes and services of their organization. Those services cover many different types of daily routines and life activities, such as health, education, transports. In this survey, we compare and classify current research works according to multiple features, including: the use of Social Network Analysis and Social Capital models, users’ motivations for participation and organizational costs, adoption of the social media platform from below. Our results show that many of these current systems are developed without taking into proper consideration the social structures and processes, with some notable and positive exceptions.

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A holistic system for troll detection on Twitter

Cited by 65 publications

References 18 publications

Towards Ethical Content-Based Detection Of Online Influence Campaigns

Towards Ethical Content-Based Detection Of Online Influence Campaigns

A Survey on Troll Detection

Adoption of Social Media in Socio-Technical Systems: A Survey

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