Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election

Caetano, Josemar; Lima, Helder Seixas; Santos, M. F.; Marques-Neto, Humberto Torres

doi:10.1186/s13174-018-0089-0

Cited by 52 publications

(34 citation statements)

References 32 publications

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“…Homophily is the tendency of the users to interact with analogous minded people having similar likes. Social media influence highlights upon the fact that attitudes of the people get affected by their peers in their social circle [8].…”

Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

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Twitter is one of the most popular social networking platforms of today’s generation and is a fundamental tool in harvesting the data of many users worldwide. Many discussions ranging from current affairs, news sharing, filing complaints to advertising and discussing some common interests etc. happen on Twitter. It is widely used by many famous politicians as a prominent communication medium to address large masses ,owing to its mass usage and popularity . Thus, it is safe to assume that people communicate their political ideologies on Twitter. Many works have been done so far to deduce the stance of user towards a particular party by performing sentiment analysis on their tweets using popular classifiers. To find connected and similar users, earlier works generated a social network graph, based on the assumption that friends and followers share similar interests, which might not be true in all cases. In contrast, the proposed work employs the concept of ensemble classifier (a single classifier generated from several base learning classifiers) to analyze the tweets and makes use of multiple interaction elements like followers/ following, mentions, re-tweets ,hash tags etc. to infer which political party a user identifies with. These interaction elements project out the homophily (users who share same beliefs and choices) amongst the users. The proposed study can be encompassed to any domain and can be used by advertising agencies, marketing companies, e-commerce, heath care etc. to identify their target audience.

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Section: Related Workmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to be able to solve the attribution problem, as described in [18], the framework includes a political affiliation classifier that aims to sort the social bots according to their political behavior. As reported in [22], [23], natural language processing (NLP) has been proved effective in analyzing the political characteristics of Twitter users. Thus, the Political Inclination classifier makes use of the average sentiment score for each group of political party's keywords (W P ).…”

Section: ) Political Inclination Classifiermentioning

confidence: 99%

Spotting Political Social Bots in Twitter: A Use Case of the 2019 Spanish General Election

Pastor-Galindo

Zago

Nespoli

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.

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“…One of the advantages of sentiment analysis is the quick and automatic overview of the opinion of the public over a certain topic. For this reason, these methods have already been applied to Twitter data in multiple instances and other works cover the subject in more detail [1][56] [5][59] [11].…”

Section: Case Studymentioning

confidence: 99%

Identifying topic relevant hashtags in Twitter streams

Figueiredo

Jorge

2019

Information Sciences

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Hashtags have become a crucial social media tool. The categorization of posts in a simple and informal manner stimulates the dissemination of content through the web. At the same time, it enables users to find messages within a specific topic of their interest. However, the flexibility provided to the user to apply any hashtag carries some problems. Equivalent expressions, like synonyms, are handled like entirely different words, while the same hashtag may refer to distinct topics. Also, many hashtags are dynamic in the sense their meaning and connections with different subjects change through time and location. This factors may hinder content discovery, specially when discussing less popular subjects. One way to overcome this problem is to provide utilities to identify relevant hashtags. Some research in hashtag recommendation in Twitter has been conducted over recent years but with greater focus on proposing hashtags for new posts instead of for a topic in general. Additionally, most of the current approaches rely on databases which require time to be assembled and rigorous maintenance to keep updated.The approach we propose for the identification of topic relevant hashtags is the development of a method to search Twitter, in real time, for hashtags relevant to a topic and represent them in a graph. For this task, we first retrieve tweets within some degrees of connection with the subject. Next, we employ Latent Dirichlet Allocation and Support Vector Machines, to classify tweets and collect their hashtags relevant to the subject. Finally, we use these hashtags to assemble a network of relations that can be used to deepen content retrieval on the original subject. This approach takes into account factors such as the popularity of the hashtags and current meaning.Furthermore, we analyze the proposed algorithm, both qualitatively and quantitatively, and compare it with past approaches, in order to evaluate its performance. The outcomes of our method are usually equal or superior to the available alternatives, in relation to the number of returned hashtags, and current relevance to the topic. However, our process is by default significantly slower than the existing alternatives.

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Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election

Cited by 52 publications

References 32 publications

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Spotting Political Social Bots in Twitter: A Use Case of the 2019 Spanish General Election

Identifying topic relevant hashtags in Twitter streams

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