Eni Mustafaraj

Bio: Eni Mustafaraj is an academic researcher from Wellesley College. The author has contributed to research in topics: Social media & Android (operating system). The author has an hindex of 16, co-authored 56 publications receiving 2051 citations. Previous affiliations of Eni Mustafaraj include University of Marburg.

How (Not) to Predict Elections

Abstract: Using social media for political discourse is increasingly becoming common practice, especially around election time Arguably, one of the most interesting aspects of this trend is the possibility of ''pulsing'' the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute Positive results have been reported in a few occasions, but without an analysis on what principle enables them This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance Second, we reveal some major challenges that limit the predictability of election results through data from social media We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow

Limits of Electoral Predictions Using Twitter

Abstract: Using social media for political discourse is becoming common practice, especially around election time One interesting aspect of this trend is the possibility of pulsing the public’s opinion about the elections, and that has attracted the interest of many researchers and the press Allegedly, predicting electoral outcomes from social media data can be feasible and even simple Positive results have been reported, but without an analysis on what principle enables them Our work puts to test the purported predictive power of socialmedia metrics against the 2010 US congressional elections Here, we applied techniques that had reportedly led to positive election predictions in the past, on the Twitter data collected from the 2010 US congressional elections Unfortunately, we find no correlation between the analysis results and the electoral outcomes, contradicting previous reports Observing that 80 years of polling research would support our findings, we argue that one should not be accepting predictions about events using social media data as a black box Instead, scholarly research should be accompanied by a model explaining the predictive power of social media, when there is one

Social Media and the Elections

Abstract: In the United States, social media sites—such as Facebook, Twitter, and YouTube—are currently being used by two out of three people ( 1 ), and search engines are used daily ( 2 ). Monitoring what users share or search for in social media and on the Web has led to greater insights into what people care about or pay attention to at any moment in time. Furthermore, it is also helping segments of the world population to be informed, to organize, and to react rapidly. However, social media and search results can be readily manipulated, which is something that has been underappreciated by the press and the general public.

The power of prediction with social media

Abstract: – Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance. , – Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided. , – A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data. , – This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.

Can collective sentiment expressed on twitter predict political elections

Abstract: Research examining the predictive power of social media (especially Twitter) displays conflicting results, particularly in the domain of political elections. This paper applies methods used in studies that have shown a direct correlation between volume/sentiment of Twitter chatter and future electoral results in a new dataset about political elections. We show that these methods display a series of shortcomings, that make them inadequate for determining whether social media messages can predict the outcome of elections.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Information credibility on twitter

Abstract: We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally.On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources.We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.

The Parable of Google Flu: Traps in Big Data Analysis

Abstract: In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?

Fake News Detection on Social Media: A Data Mining Perspective

Abstract: Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

The rise of social bots

Abstract: Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.