2016 Election Results

ASHA Leader ◽  
2016 ◽  
Vol 21 (7) ◽  
pp. 58-58
Keyword(s):  
2016 Election ◽  
Election Results

scholarly journals Arbitrage in Political Prediction Markets

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Andrew Stershic ◽  
Kritee Gujral
Keyword(s):  
Prediction Markets ◽  
Sporting Events ◽  
Market Pricing ◽  
Prediction Tools ◽  
Price Distortion ◽  
2016 Election ◽  
Online Prediction ◽  
Election Results ◽  
Uncertain Outcomes

Online prediction markets are a powerful tool for aggregating information and show promise as predictive tools for uncertain outcomes, from sporting events to election results. However, these markets only serve as effective prediction tools so long as the market pricing remains efficient. We analyze the potential arbitrage profits derived from such mispricings in two leading American political prediction markets, PredictIt (for the 2016 and 2020 elections) and the Iowa Electronic Markets (for the 2016 election), to quantify the degree of mispricing and to show how market design can contribute to price distortion. We show that contracts hosted by PredictIt, compared to the IEM, are chronically mispriced, with large arbitrage profits in the 2016 election markets and non-negligible profits for the 2020 markets. We discuss the role of profit fees and contract limits, the primary differences between the PredictIt and IEM, in distorting pricing on PredictIt by limiting the ability of traders to capture arbitrage profits. Additionally, we examine the association between arbitrage and margin-linking, increased liquidity, and the number of unique contracts PredictIt's markets. This research provides cautionary evidence of potential inefficiencies in prediction markets with the intention of improving market implementation and enhancing market predictiveness.

The Results of Exit Polls in Kansas to Verify Voting Machine Counts in the November 2016 Election

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Elizabeth Clarkson
Keyword(s):  
Response Bias ◽  
Participation Rates ◽  
Exit Poll ◽  
Expected Outcome ◽  
2016 Election ◽  
Volunteer Labor ◽  
High Participation ◽  
Show Evidence ◽  
Election Results ◽  
Voting Machine

Abstract Citizens’ exit polls are performed by local voters to verify the official reported election results. Five citizens’ exit polls were run in southeast Kansas during the Nov 8th 2016 election. These exit polls were designed specifically to verify computer generated vote counts and run solely by volunteer labor, all local citizens who were willing to put in the necessary hours on Election Day to conduct the poll and later, to count the results by hand. These exit polls were able to obtain high participation rates resulting in the ability to detect small yet statistically significant differences. All five polling stations surveyed show evidence of multiple statistical anomalies in both the pattern and size of the errors between the official results and exit poll results although biases were not uniformly oriented across sites. The small discrepancies found in the studied races were insufficient to alter the outcomes. Non-response bias and unintentional errors were evaluated as potential causes; those explanations were plausible in some but not all cases. These results show a pattern of discrepancies between the exit polls and computer counted results displaying consistent bias within sites. This would be an expected outcome of a deliberate manipulation of the computer results. While this data doesn’t conclusively prove election interference and manipulation of votes counts, it should be taken seriously as a sign of such interference. Doubts about the accuracy of the reported results are appropriate unless other plausible explanations for the discrepancies can be found.

scholarly journals Will I die of coronavirus? Google Trends data reveal that politics determine virus fears

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258189
Author(s):  
Joan C. Timoneda ◽  
Sebastián Vallejo Vera
Keyword(s):  
General Population ◽  
Epidemiological Data ◽  
Google Trends ◽  
Search Queries ◽  
Search Volume ◽  
Political Preferences ◽  
2016 Election ◽  
Election Results ◽  
Epidemiological Trends ◽  
Abundant Data

Is Google Trends (GT) useful to survey populations? Extant work has shown that certain search queries reflect the attitudes of hard-to-survey populations, but we do not know if this extends to the general population. In this article, we leverage abundant data from the Covid-19 pandemic to assess whether people’s worries about the pandemic match epidemiological trends as well as political preferences. We use the string ‘will I die from coronavirus’ on GT as the measure for people’s level of distress regarding Covid-19. We also test whether concern for coronavirus is a partisan issue by contrasting GT data and 2016 election results. We find strong evidence that (1) GT search volume close matches epidemiological data and (2) significant differences exist between states that supported Clinton or Trump in 2016.

scholarly journals Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2082
Author(s):  
Hassan Nazeer Chaudhry ◽  
Yasir Javed ◽  
Farzana Kulsoom ◽  
Zahid Mehmood ◽  
Zafar Iqbal Khan ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Bayes Classifier ◽  
Before And After ◽  
2016 Election ◽  
Global Events ◽  
Election Results ◽  
The Right ◽  
The Masses ◽  
Public Views

U.S. President Joe Biden took his oath after being victorious in the controversial U.S. elections of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic following delays of the announcement of the election’s results. Donald J. Trump claimed that there was potential rigging against him and refused to accept the results of the polls. The sentiment analysis captures the opinions of the masses over social media for global events. In this work, we analyzed Twitter sentiment to determine public views before, during, and after elections and compared them with actual election results. We also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election. We created a dataset using tweets’ API, pre-processed the data, extracted the right features using TF-IDF, and applied the Naive Bayes Classifier to obtain public opinions. As a result, we identified outliers, analyzed controversial and swing states, and cross-validated election results against sentiments expressed over social media. The results reveal that the election outcomes coincide with the sentiment expressed on social media in most cases. The pre and post-election sentiment analysis results demonstrate the sentimental drift in outliers. Our sentiment classifier shows an accuracy of 94.58% and a precision of 93.19%.

The 2016 Pennsylvania Presidential and U.S. Senate Elections: Breaking Pennsylvania’s Electoral Habits

Commonwealth ◽  
2017 ◽  
Vol 19 (2) ◽  
Author(s):  
Berwood Yost ◽  
Jackie Redman ◽  
Scottie Thompson
Keyword(s):  
Survey Data ◽  
Voter Turnout ◽  
Voter Registration ◽  
Current Direction ◽  
Senate Elections ◽  
2016 Election ◽  
Election Results

This article uses pre-election survey data, post-election survey data, and voter registration and election data to interpret the outcomes of the 2016 presidential and U.S. Senate races in Pennsylvania. This analysis shows how changes in voter registration and voter turnout in specific areas of the Commonwealth, driven in large part by less-educated voters, those dissatisfied with the current direction of the country, and the performance of the incumbent president, explain the 2016 election results. 

scholarly journals The 2016 Elections: Impact on the Work of Charitable Nonprofits

2016 ◽  
Vol 7 (4) ◽  
pp. 573-579
Author(s):  
Tim Delaney ◽  
David L. Thompson
Keyword(s):  
Local State ◽  
Foreseeable Future ◽  
Policy Decisions ◽  
Private Foundations ◽  
2016 Election ◽  
Election Results

AbstractElections have consequences. This article analyzes the 2016 election results, previews how some of the policy decisions made across the multidimensional local, state, and federal levels of governments and made by officials across the executive, judicial, and legislative branches of governments could affect the work of charitable nonprofits and private foundations, and emphasizes that the advocacy function of nonprofits is going to be more important than ever in the foreseeable future.

Everybody Follows the Crowd?

2017 ◽  
Vol 29 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Magdalena Obermaier ◽  
Thomas Koch ◽  
Christian Baden
Keyword(s):  
Experimental Design ◽  
News Coverage ◽  
Personal Characteristics ◽  
Election Campaigns ◽  
Bandwagon Effect ◽  
Political News ◽  
Election Results ◽  
Opinion Polls ◽  
Electoral Preferences

Abstract. Opinion polls are a well-established part of political news coverage, especially during election campaigns. At the same time, there has been controversial debate over the possible influences of such polls on voters’ electoral choices. The most prominent influence discussed is the bandwagon effect: It states that voters tend to support the expected winner of an upcoming election, and use polls to determine who the likely winner will be. This study investigated the mechanisms underlying the effect. In addition, we inquired into the role of past electoral performances of a candidate and analyzed how these (as well as polls) are used as heuristic cues for the assessment of a candidate’s personal characteristics. Using an experimental design, we found that both polls and past election results influence participants’ expectations regarding which candidate will succeed. Moreover, higher competence was attributed to a candidate, if recipients believe that the majority of voters favor that candidate. Through this attribution of competence, both information about prior elections and current polls shaped voters’ electoral preferences.

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