In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user's attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best performance obtained by the ML classifier was an AUC of 0.8, an accuracy of 0.812, precision of 0.819, and recall of 0.966. In addition, as opposed to previous studies, we found that clickbait post titles are statistically significant shorter than legitimate post titles. Finally, we found that counting the number of formal English words in the given content is useful for clickbait detection.
@cite_35 and @cite_21 used ML techniques for bot detection. @cite_35 based their detection on sentiment analysis, social network analysis, posted content, and account property features. @cite_21 presented BotOrNot, a bot identification platform that can be used through a Web user interface. They detected bots based on all of the features used by @cite_35 , as well as behavior features.
@cite_35 @cite_21
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This paper describes our submissions to the Aspect Based Sentiment Analysis task of SemEval-2016. For Aspect Category Detection (Subtask1 Slot1), we used multiple ensembles, based on Support Vector Machine classifiers. For Opinion Target Expression extraction (Subtask1 Slot2), we used a sequence labeling approach with Conditional Random Fields. For Polarity Detection (Subtask1 Slot3), we used an ensemble of two supervised classifiers, one based on hand crafted features and one based on word embeddings. Our systems were ranked in the top 6 positions in all the tasks we participated. The source code of our systems is publicly available. While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots. Since its release in May 2014, BotOrNot has served over one million requests via our website and APIs. The identification of automated activitiy in social media, specifically the detection of social bots, has become one of the major tasks within the field of social media computation. Recently published classification algorithms and frameworks focus on the identification of single bot accounts. Within different Twitter experiments, we show that these classifiers can be bypassed by hybrid approaches, which on a first glance may motivate further research for more sophisticated techniques. However, we pose the question, whether the detection of single bot accounts is a necessary condition for identifying malicious, strategic attacks on public opinion. Or is it more productive to concentrate on detecting strategies? Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9 and 15 of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.