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.

Social media today provide an impressive amount of data about users and their societal interactions, thereby offering computer and social scientists, economists, and statisticiansamong others-many new opportunities for research exploration. Arguably, one of the most interesting lines of work is that of predicting future events and developments based on social media data, as we have recently seen in the areas of politics, finance, entertainment, market demands, health, etc. In fact, an average of one in seven research papers presented at the WWW, But what can be successfully predicted and why? Since the first algorithms and techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains.Better understanding the predictive power and limitations of social media is therefore of utmost importance, in order to be successful and avoid false expectations, misinformation or unintended consequences. Today, current methods and techniques are far from being well understood, and it is mostly unclear to what extent or under what conditions the different methods for prediction can be applied to social media. While there exists a respectable and growing amount of literature in this area, current work is fragmented, characterized by a lack of commonly accepted evaluation approaches. Yet, this research seems to have reached a sufficient level of interest and relevance to justify a dedicated section.This special section aims to shape a frame of important questions to be addressed in this field, and fill the gaps in current research with presentations of early research on algorithms, techniques, methods and empirical studies aimed at the prediction of future or current events based on user-generated content in social media.2

Manipulation of social media affects perceptions of candidates and compromises decision-making.

No abstract

In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" [8] won the Best Paper Prize of the Web Science 2010 Conference. Among its findings were the discovery and documentation of what was termed a "Twitter-bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can increase their impact on real-world societal issues.