Polling Voting Intentions

Kellner, Peter; Twyman, Joe; Wells, Anthony

doi:10.1057/9780230305045_7

Cited by 6 publications

(5 citation statements)

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“…There is a general reluctance of opinion pollsters (with some exceptions) to countenance the presentation of qualitative research in the public sphere. However, semantic Nick Anstead and Ben O'Loughlin 271 debates, arguing that they undermined attempts by political elites to 'spin' the result of the debate and claim victory for their own side (Kellner et al 2011;Lawes and Hawkins 2011).…”

Section: Benjamin J Leementioning

confidence: 98%

Analyzing Social Media Data and Web Networks

Gibson¹,

Cantijoch²,

Ward³

2014

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Section: Benjamin J Leementioning

confidence: 98%

Analyzing Social Media Data and Web Networks

Gibson¹,

Cantijoch²,

Ward³

2014

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“…Finding items with maximum and minimum frequencies in a stream correspond to finding winners under plurality and veto voting rules respectively in the context of voting 2 [BCE + 15]. The streaming aspect of voting could be crucial in applications like online polling [KTW11], recommender systems [RV97, HKTR04,AT05] where the voters are providing their votes in a streaming fashion and at every point in time, we would like to know the popular items. While in some elections, such as for political positions, the scale of the election may not be large enough to require a streaming algorithm, one key aspect of these latter votingbased problems is that they are rank-based which is useful when numerical scores are not available.…”

Section: Motivations For Variants Of Heavy Hittersmentioning

confidence: 99%

An Optimal Algorithm for ℓ ₁ -Heavy Hitters in Insertion Streams and Related Problems

Bhattacharyya¹,

Dey

Woodruff³

2018

ACM Trans. Algorithms

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We give the first optimal bounds for returning the ℓ1-heavy hitters in a data stream of insertions, together with their approximate frequencies, closing a long line of work on this problem. For a stream of m items in {1, 2, . . . , n} and parameters 0 < ε < ϕ 1, let fi denote the frequency of item i, i.e., the number of times item i occurs in the stream. With arbitrarily large constant probability, our algorithm returns all items i for which fi ϕm, returns no items j for which fj (ϕ − ε)m, and returns approximationsfi with |fi − fi| εm for each item i that it returns. Our algorithm uses O(ε −1 log ϕ −1 + ϕ −1 log n + log log m) bits of space, processes each stream update in O(1) worst-case time, and can report its output in time linear in the output size. We also prove a lower bound, which implies that our algorithm is optimal up to a constant factor in its space complexity. A modification of our algorithm can be used to estimate the maximum frequency up to an additive εm error in the above amount of space, resolving Question 3 in the IITK 2006 Workshop on Algorithms for Data Streams for the case of ℓ1-heavy hitters. We also introduce several variants of the heavy hitters and maximum frequency problems, inspired by rank aggregation and voting schemes, and show how our techniques can be applied in such settings. Unlike the traditional heavy hitters problem, some of these variants look at comparisons between items rather than numerical values to determine the frequency of an item.

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“…Reaching a representative sample of the voter-eligible population is a challenge for political polls, both online and off (Cook, Heath, and Thompson 2000; Diaz et al 2016; Huberty 2015; Kellner, Twyman, and Wells 2011). The classic example is that of the Literary Digest Poll concerning the 1936 U.S. presidential election.…”

Section: Challenges Of Online Political Pollingmentioning

confidence: 99%

Biases of Online Political Polls: Who Participates?

Hargittai

Karaoglu

2018

Socius: Sociological Research for a Dynamic World

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With a large portion of the population online and the high cost of phone-based surveys, querying people about their voter preference online can offer an affordable and timely alternative. However, given that there are biases in who adopts various sites and services that are often used as sampling frames (e.g., various social media), online political polls may not represent the views of the overall population. How are such polls biased? Who is most likely to participate in them? Drawing on a national survey of voter-eligible American adults administered in summer 2016, this paper shows that background characteristics (i.e., age, gender, race, education, and employment status) as well as Internet experiences and skills relate to who casts votes in online political polls.

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Polling Voting Intentions

Cited by 6 publications

References 0 publications

Analyzing Social Media Data and Web Networks

Analyzing Social Media Data and Web Networks

An Optimal Algorithm for ℓ ₁ -Heavy Hitters in Insertion Streams and Related Problems

Biases of Online Political Polls: Who Participates?

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Polling Voting Intentions

Cited by 6 publications

References 0 publications

Analyzing Social Media Data and Web Networks

Analyzing Social Media Data and Web Networks

An Optimal Algorithm for ℓ 1 -Heavy Hitters in Insertion Streams and Related Problems

Biases of Online Political Polls: Who Participates?

Contact Info

Product

Resources

About

An Optimal Algorithm for ℓ ₁ -Heavy Hitters in Insertion Streams and Related Problems