Predictions based on Twitter &amp;#x2014; A critical view on the research process

Madlberger, Lisa; Almansour, Amai

doi:10.1109/icodse.2014.7062667

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Twitter offers to business users the possibility to integrate its analytics with audience measurement tools and services, such as Nielsen Digital Ad Ratings (DAR) and ComScore validated Campaign Essentials (vCE). Overviews of predictive methods exploiting tweets have been proposed in the works of Sikdar et al [52], Madlberger and Almansour [37], Zaman et al [61]. In most cases, the predictive capabilities of Twitter data have been identified by using volume metrics on tweets (i.e., the total number of tweets and/or retweets associated with a Twitter user or presenting a certain hashtag).…”

Section: Introductionmentioning

confidence: 99%

Assessing the reTweet proneness of tweets: predictive models for retweeting

Nesi

Pantaleo

Paoli

et al. 2018

Multimed Tools Appl

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The problem of assessing the mechanisms underlying the phenomenon of virality of social network posts is of great value for many activities, such as advertising and viral marketing, influencing and promoting, early monitoring and emergency response. Among the several social networks, Twitter.com is one of the most effective in propagating information in real time, and the propagation effectiveness of a post (i.e., tweet) is related to the number of times the tweet has been retweeted. Different models have been proposed in the literature to understand the retweet proneness of a tweet (tendency or inclination of a tweet to be retweeted). In this paper, a further step is presented, thus several features extracted from Twitter data have been analyzed to create predictive models, with the aim of predicting the degree of retweeting of tweets (i.e., the number of retweets a given tweet may get). The main goal is to obtain indications about the probable number of retweets a tweet may obtain from the social network. In the paper, the usage of the classification trees with recursive partitioning procedure for prediction has been proposed and the obtained results have been compared, in terms of accuracy and processing time, with respect to other methods. The Twitter data employed for the proposed study have been collected by using the Twitter Vigilance study and research platform of DISIT Lab in the last 18 months. The work has been developed in the context of smart city projects of the European Commission RESOLUTE H2020, in which the

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Section: Introductionmentioning

confidence: 99%

Assessing the reTweet proneness of tweets: predictive models for retweeting

Nesi

Pantaleo

Paoli

et al. 2018

Multimed Tools Appl

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“…A more comprehensive overview of predictive methods exploiting social media analysis can be found in [13]. In this work, the authors have criticised the predictive capabilities of some proposed models adopting specific filtering or classifications based on human assessors, thus reducing the replicability of the solution.…”

Section: Related Workmentioning

confidence: 99%

The Conundrum of Success in Music: Playing it or Talking About it?

et al. 2019

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Nowadays social media are the main means for conducting discussions and sharing opinions. The huge amount of information generated by social media users is helpful for predicting outcomes of real-world events in different fields, including business, politics and the entertainment industry. In this paper, we studied the possibility of forecasting the success of music albums by analyzing heterogeneous data sources spanning from social media (Twitter, Instagram and Facebook) to mainstream American newspapers (e.g., New York Times, Rolling Stones). The idea is to exploit music albums' pre-release hype and post-release approval to predict the album's rank with reference to the well-known Billboard 200 album chart, which tabulates the weekly popularity of music albums in the USA. To predict the success of a music album, that is its rank in the chart, we identified metrics based on the messages' posting trend, the variation of the sentiment associated to such messages, the number of followers of the album's author, and the importance of the people who talk about it. To evaluate the effectiveness of the proposed metrics we have compared the prediction performances of several models based on supervised learning approaches among those most used in literature. As a result, we obtained that the Random Forest approach is able to predict the music album rank in the Billboard 200 Chart with an expected accuracy of 97%. As a further validation, using this specific model, we also conducted an additional real usage test obtaining an almost matching result (accuracy of 94%).INDEX TERMS Social media, machine learning, prediction, sentiment analysis, music industry.We design a machine learning-based prediction model that we call Billboard 200 Predictor, or BB200P.

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“…SSA involves training a model with texts examples in order to summarize their syntactic structures using their words or sentences as features. Scholars that are familiar with Python can easily train a naïve Bayes classifier for positive/negative previous labeled messages (training set) taking only adjectives as features for the models in few lines adapted from Kinsley (2017) in any case, even when SSA has not been completely accurate, nor has provided relevant or influential findings in twitter analysis (Madlberger & Almansour, 2014), this technique has been recently applied to predict events in various domains Preethi & Uma, 2015) such as finance (Smailović et al, 2013), social networks (Sluban et al, 2015) and election results (Smailović et al, 2015), with a more than promising prediction accuracy. in fact, in the last decade there has been an ever-increasing growth of SSA, particularly in the social and political science domain.…”

Section: Supervised Sentiment Analysis (Ssa)mentioning

confidence: 99%

Distributed Supervised Sentiment Analysis of Tweets: Integrating Machine Learning and Streaming Analytics for Big Data Challenges in Communication and Audience Research

Calderón¹,

Mohedano²,

Mariño³

2019

EMP

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The large-scale analysis of tweets in real-time using supervised sentiment analysis depicts a unique opportunity for communication and audience research. Bringing together machine learning and streaming analytics approaches in a distributed environment might help scholars to obtain valuable data from Twitter in order to immediately classify messages depending on the context with no restrictions of time or storage, empowering cross-sectional, longitudinal and experimental designs with new inputs. Even when communication and audience researchers begin to use computational methods, most of them remain unfamiliar with distributed technologies to face big data challenges. This paper describes the implementation of parallelized machine learning methods in Apache Spark to predict sentiments in real-time tweets and explains how this process can be scaled up using academic or commercial distributed computing when personal computers do not support computations and storage. We discuss the limitation of these methods and their implications in communication, audience and media studies.El análisis a gran escala de tweets en tiempo real utilizando el análisis de sentimiento supervisado representa una oportunidad única para la investigación de comunicación y audiencias. El poner juntos los enfoques de aprendizaje automático y de analítica en tiempo real en un entorno distribuido puede ayudar a los investigadores a obtener datos valiosos de Twitter con el fin de clasificar de forma inmediata mensajes en función de su contexto, sin restricciones de tiempo o almacenamiento, mejorando los diseños transversales, longitudinales y experimentales con nuevas fuentes de datos. A pesar de que los investigadores de comunicación y audiencias ya han comenzado a utilizar los métodos computacionales en sus rutinas, la mayoría desconocen el uso de las tecnologías de computo distribuido para afrontar retos de dimensión big data. Este artículo describe la implementación de métodos de aprendizaje automático paralelizados en Apache Spark para predecir sentimientos de tweets en tiempo real y explica cómo este proceso puede ser escalado usando computación distribuida tanto comercial como académica, cuando los ordenadores personales son insuficientes para almacenar y analizar los datos. Se discuten las limitaciones de estos métodos y sus implicaciones en los estudios de medios, comunicación y audiencias.

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Predictions based on Twitter — A critical view on the research process

Cited by 11 publications

References 22 publications

Assessing the reTweet proneness of tweets: predictive models for retweeting

Assessing the reTweet proneness of tweets: predictive models for retweeting

The Conundrum of Success in Music: Playing it or Talking About it?

Distributed Supervised Sentiment Analysis of Tweets: Integrating Machine Learning and Streaming Analytics for Big Data Challenges in Communication and Audience Research

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