Analysis of the Influence of Internet TV Station on Wikipedia Page Views

We aim to investigate the influence of television on the web; if the influence is strong, a viral effect may be expected. In this paper, we focus on the Internet TV station and on Wikipedia use as exploratory behavior on the web. We analyzed the influence of Internet TV station on Wikipedia page views. Our aim is to clarify the characteristics of page views as related to Internet TV station in order to index outward impact and develop a prediction model. The results indicate that there is a correlation between TV viewership and page views. Moreover we find that the time lag between TV and web gradually reduce as broadcasts begin after 9:00; after 23:00, page views tend to be maximized during the broadcast itself. We also differentiate between page views on PC and on mobile and find that PC pages tend to be accessed more during the daytime. In addition, we consider the number of broadcasts per program, and observe that viewership tends to stabilize as the number of broadcasts increases but that page views tend to decrease.Comment: The 3rd International Workshop on Application of Big Data for Computational Social Science (ABCSS2018

Discovery, retrieval, and analysis of the 'Star wars' botnet in twitter

It is known that many Twitter users are bots, which are accounts controlled and sometimes created by computers. Twitter bots can send spam tweets, manipulate public opinion and be used for online fraud. Here we report the discovery, retrieval, and analysis of the ‘Star Wars’ botnet in Twitter, which consists of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The botnet contains a single type of bot, showing exactly the same properties throughout the botnet. It is unusually large, many times larger than other available datasets. It provides a valuable source of ground truth for research on Twitter bots. We analysed and revealed rich details on how the botnet was designed and created. As of this writing, the Star Wars bots are still alive in Twitter. They have survived since their creation in 2013, despite the increasing efforts in recent years to detect and remove Twitter bots. We also reflect on the ‘unconventional’ way in which we discovered the Star Wars bots, and discuss the current problems and future challenges of Twitter bot detection

It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia

Wikipedia is a community-created encyclopedia that contains information about notable people from different countries, epochs and disciplines and aims to document the world's knowledge from a neutral point of view. However, the narrow diversity of the Wikipedia editor community has the potential to introduce systemic biases such as gender biases into the content of Wikipedia. In this paper we aim to tackle a sub problem of this larger challenge by presenting and applying a computational method for assessing gender bias on Wikipedia along multiple dimensions. We find that while women on Wikipedia are covered and featured well in many Wikipedia language editions, the way women are portrayed starkly differs from the way men are portrayed. We hope our work contributes to increasing awareness about gender biases online, and in particular to raising attention to the different levels in which gender biases can manifest themselves on the web

The influence of a start-up process on the entrepreneurs’ emotions, deduced by their Twitter accounts

In this paper, entrepreneurship has been analysed along a temporal range, stressing the influence that being founded has on an entrepreneur’s emotions. Starting from a vast base of Twitter accounts, through the text analysis, have been extracted ratios concerning the presence within the Tweets of a positive and negative emotions. Those ratios have been calculated equating the amount of specific words referring to that emotions on the total amount of words tweeted every year by the entrepreneurs. Those people are mostly from US, and the investors founding their ideas undertook many different investment strategies. The main focus lays within the effect that the overall founding strategy has on the entrepreneurs’ emotions. The purpose is comparing those emotional ratios with the verification of the founding process. This aims to test how the emotions are affected by the fact that the start-up has been financed, or it has not received any funds. The results have been analysed through Stata, yielding interest findings shown along the whole paper.Nesta dissertação, o empreendedorismo foi analisado dentro de um intervalo de tempo, de modo a fazer entender a maneira como o financiamento influencia as emoções dos empreendedores. Como ponto de partida, vários textos de uma vasta base de contas de Twitter foram analisados com o objectivo de construir rácios que destaquem a presença de emoções positivas e negativas nos Tweets. Estes rácios foram calculados equacionando o montante específico de palavras referentes a este tipo de emoções sobre o montante total de palavras trocadas nos Tweets todos os anos pelos empreendedores. A amostra de empreendedores é maioritariamente dos EUA e os investidores que financiaram as suas ideias utilizaram várias estratégias diferentes de investimento. O propósito é comparar estes “rácios emocionais” com os seus processos de financiamento. Assim sendo, o objetivo é testar como é que as emoções são afetadas pelo facto da start-up ser financiada ou não receber qualquer tipo de fundos. Os resultados foram analisados através do programa Stata e são responsáveis por várias conclusões interessantes que são discutidas ao longo da dissertação

From 2,772 segments to five personas: Summarizing a diverse online audience by generating culturally adapted personas

Understanding users in the era of social media is challenging, requiring organizations to adopt novel computation-aided approaches. To exemplify such an approach, we retrieved information on millions of interactions with YouTube video content from a major Middle Eastern media outlet, to automatically generate personas that capture how different audience segments interact with thousands of individual content pieces. Then, we used qualitative data to provide additional insights into the automatically generated persona profiles. Our findings provide insights into social media usage in the Middle East and demonstrate the application of a novel methodology that generates culturally adapted personas of social media audiences, summarizing complex social analytics data into human portrayals that are easy to understand by end users in real organizations.</p

Estimating attention flow in online video networks

© 2019 Association for Computing Machinery. Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about the impacts of the recommender systems. In this paper, we first construct the Vevo network — a YouTube video network with 60,740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics. This results in a total of 310 million views every day over a period of 9 weeks. Next, we present large-scale measurements that connect the structure of the recommendation network and the video attention dynamics. We use the bow-tie structure to characterize the Vevo network and we find that its core component (23.1% of the videos), which occupies most of the attention (82.6% of the views), is made out of videos that are mainly recommended among themselves. This is indicative of the links between video recommendation and the inequality of attention allocation. Finally, we address the task of estimating the attention flow in the video recommendation network. We propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines. This model also identifies a group of artists gaining attention because of the recommendation network. Altogether, our observations and our models provide a new set of tools to better understand the impacts of recommender systems on collective social attention

Social Clicks: What and Who Gets Read on Twitter?

International audienceOnline news domains increasingly rely on social media to drive traffic to their websites. Yet we know surprisingly little about how a social media conversation mentioning an online article actually generates clicks. Sharing behaviors, in contrast, have been fully or partially available and scrutinized over the years. While this has led to multiple assumptions on the diffusion of information, each assumption was designed or validated while ignoring actual clicks. We present a large scale, unbiased study of social clicks - that is also the first data of its kind - gathering a month of web visits to online resources that are located in 5 leading news domains and that are mentioned in the third largest social media by web referral (Twitter). Our dataset amounts to 2.8 million shares, together responsible for 75 billion potential views on this social media, and 9.6 million actual clicks to 59,088 unique resources. We design a reproducible methodology and carefully correct its biases. As we prove, properties of clicks impact multiple aspects of information diffusion, all previously unknown. (i) Secondary resources, that are not promoted through headlines and are responsible for the long tail of content popularity, generate more clicks both in absolute and relative terms. (ii) Social media attention is actually long-lived, in contrast with temporal evolution estimated from shares or receptions. (iii) The actual influence of an intermediary or a resource is poorly predicted by their share count, but we show how that prediction can be made more precise