AcaWiki - User contributions [en]

The Many Meanings of Research Utilization

2018-04-15T20:18:31Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary=In the social sciences, researchers often grapple with concerns about whether or not their work actually affects policy decisions. The common mode of thought is that social science research is meant to address a concrete question. It's hard to explain, then, why such research often doesn't get implemented. Weiss argues that such a a model of research is not accurate; indeed, there are actually seven models to consider for how research is used by policy makers.

==Research Models==
The models for how social science research is actually used are defined below:
# Knowledge-driven model: Research advances public policy by the fact that it produces knowledge. However, unless the question that the research addresses is agreed upon to be important, it is unlikely that the research will have much of an effect on policymakers.
# Problem-solving model: The most common view is that research advances public policy by answering some concrete question. However, this is not always the case because public policy depends on a number of circumstances beyond research.
# Interactive model: Social scientists alone does not influence public policy. It is also dependent on aides, journalists, and interest groups.
# Political model: Research is used by policy makers as "ammunition" for partisan positions.
# Tactical model: Research is used to deflect demands for action from the public.
# Enlightenment model: Research spreads different perspectives and concepts to among policy makers, rather than directly influencing decisions.
# Intellectual enterprise of society: Research interacts in a back-and-forth loop with policy in response to the interests of society.

==Notable References==
Sherwin, Chalmers William, and Raymond S. Isenson. First interim report on Project Hindsight (summary). Office of the Director of Defense Research and Engineering Washington DC, 1966.

Caplan, Nathan. "The Use of Social Science Knowledge in Policy Decisions at the National Level: A Report to Respondents." (1975).
|relevance=These models for how social science research is "used" can inform researchers in how to make better contributions. Instead of just thinking about what policy questions they can answer, they should instead consider the wider possible implications of their work. Doing so will help social scientists make a larger impact on pressing issues.
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T20:15:01Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary=In the social sciences, researchers often grapple with concerns about whether or not their work actually affects policy decisions. The common mode of thought is that social science research is meant to address a concrete question. It's hard to explain, then, why such research often doesn't get implemented. Weiss argues that such a a model of research is not accurate; indeed, there are actually seven models to consider for how research is used by policy makers.

==Research Models==
The models for how social science research is actually used are defined below:
# Knowledge-driven model: Research advances public policy by the fact that it produces knowledge. However, unless the question that the research addresses is agreed upon to be important, it is unlikely that the research will have much of an effect on policymakers.
# Problem-solving model: The most common view is that research advances public policy by answering some concrete question. However, this is not always the case because public policy depends on a number of circumstances beyond research.
# Interactive model: Social scientists alone does not influence public policy. It is also dependent on aides, journalists, and interest groups.
# Political model: Research is used by policy makers as "ammunition" for partisan positions.
# Tactical model: Research is used to deflect demands for action from the public.
# Enlightenment model: Research spreads different perspectives and concepts to among policy makers, rather than directly influencing decisions.
# Intellectual enterprise of society: Research interacts in a back-and-forth loop with policy in response to the interests of society.

==Notable References==
|relevance=These models for how social science research is "used" can inform researchers in how to make better contributions. Instead of just thinking about what policy questions they can answer, they should instead consider the wider possible implications of their work. Doing so will help social scientists make a larger impact on pressing issues.
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T20:07:31Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary=In the social sciences, researchers often grapple with concerns about whether or not their work actually affects policy decisions. The common mode of thought is that social science research is meant to address a concrete question. It's hard to explain, then, why such research often doesn't get implemented. Weiss argues that such a a model of research is not accurate; indeed, there are actually seven models to consider for how research is used by policy makers.

==Research Models==
The models for how social science research is actually used are defined below:
# Knowledge-driven model: Research advances public policy by the fact that it produces knowledge. However, unless the question that the research addresses is agreed upon to be important, it is unlikely that the research will have much of an effect on policymakers.
# Problem-solving model: The most common view is that research advances public policy by answering some concrete question. However, this is not always the case because public policy depends on a number of circumstances beyond research.
# Interactive model: Social scientists alone does not influence public policy. It is also dependent on aides, journalists, and interest groups.
# Political model: Research is used by policy makers as "ammunition" for partisan positions.
# Tactical model: Research is used to deflect demands for action from the public.
# Enlightenment model: Research spreads different perspectives and concepts to among policy makers, rather than directly influencing decisions.
# Intellectual enterprise of society: Research interacts in a back-and-forth loop with policy in response to the interests of society.

==Conclusion==

==Notable References==
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T19:59:39Z

Ahounsel:

The Many Meanings of Research Utilization

2018-04-15T19:52:10Z

Ahounsel:

The Many Meanings of Research Utilization

2018-04-15T19:49:05Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary===Problem==
In the social sciences, researchers often grapple with concerns about whether or not their work actually affects policy decisions. The common mode of thought is that social science research is meant to address a concrete question. It's hard to explain, then, why such research often doesn't get implemented. Weiss argues that such a a model of research is not accurate; indeed, there are actually seven models to consider for how research is used by policy makers.

==Research Models==

==Notable References==
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T19:48:15Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary===Problem==
In the social sciences, researchers often grapple with concerns about whether or not their work actually affects policy decisions. The common mode of thought is that social science research is meant to address a concrete question. It's hard to explain, then, why such research often doesn't get implemented. Weiss argues that such a a model of research is not accurate; indeed, there are actually seven models to consider for how research can affect policy decisions.

==Research Models==

==Notable References==
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T19:47:25Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary===Problem==
In the social sciences, researchers often grapple with concerns about whether or not their work actually affects policy decisions. The common mode of thought is that social science research is meant to address a policy question. It's hard to explain, then, why such research often doesn't get implemented. Weiss argues that such a a model of research is not accurate; indeed, there are actually seven models to consider for how research can affect policy decisions.

==Research Models==

==Notable References==
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T19:43:03Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary===Problem==

==Research Models==

==Notable References==
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T19:42:06Z

Ahounsel:

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|summary===Problem==

==Design==

==Methods==

==Results==

==Notable References==
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

The Many Meanings of Research Utilization

2018-04-15T19:40:30Z

Ahounsel: Replaced content with "{{Summary |title=The Many Meanings of Research Utilization |authors=Weiss, Carol |tags=Social Science |journal=Public administration review |pub_date=1979 |subject=Sociolo..."

{{Summary
|title=The Many Meanings of Research Utilization
|authors=Weiss, Carol
|tags=Social Science
|journal=Public administration review
|pub_date=1979
|subject=Sociology
}}

Changing climates of conflict: A social network experiment in 56 schools

2018-04-02T02:51:31Z

Ahounsel:

{{Summary
|title=Changing climates of conflict: A social network experiment in 56 schools
|authors=Elizabeth Paluck, Hana Sheperd, Peter Aronow
|tags=Social Influence, Social Norms, Bullying, Adolescents, Social Psychology
|summary=It has been proposed that humans base their behavior on perceived social norms in the communities that they are situated in. Paluck et. al evaluate this theory by developing an experiment on conflict reduction in New Jersey schools. They find that when students encourage conflict-reducing behavior among their online social networks, there is a reduction of disciplinary reports by 30% over 1 year.

==Problem==
Several approaches have been proposed for reducing negative behavior and/or encouraging positive behavior across a community. However, much of the debate has been on a theoretical level rather than a practical level. As such, it is not clear what approaches should be used in real-world situations.

==Design==
To determine how community-wide behavior can be altered in the real world, Paluck designed an experiment for reducing conflict among students in New Jersey schools. Her experiment incorporates three popular theories:
# Developing behavioral interventions that are targeted towards an individual's psychology
# Scaling up behavioral interventions with regulation or community-level persuasion
# Altering behavior through peer influence

==Methods==
28 of 56 New Jersey schools were randomly selected to receive the intervention. Within these schools, a group of students were randomly selected that represented 15% of the school's population, and half of these students were selected to participate in the experiment. These students would go to optional weekly meetings to develop strategies for conflict reduction with the assistance of a trained researcher. The students would then encourage their peers via social media to adopt these behaviors.

==Results==
On average, the intervention reduced the number of disciplinary reports by 30%. Furthermore, students that were identified among their peers as "social referents" had an even greater effect on encouraging conflict-reducing behaviors. When 20% of the students that received the intervention in a given school were identified as social referents, the school experienced a 60% reduction in disciplinary reports. Lastly, when social referents received the intervention, other students were less likely to believe that conflict is a normal thing. These results show that social referents are best equipped to spread conflict-reducing behaviors across a community through peer influence.

==Notable References==
Paluck EL, Shepherd H (2012) The salience of social referents: A field experiment on
collective norms and harassment behavior in a school social network. J Pers Soc
Psychol 103(6):899–915.

Ennett ST, Tobler NS, Ringwalt CL, Flewelling RL (1994) How effective is drug abuse
resistance education? A meta-analysis of Project DARE outcome evaluations. Am J
Public Health 84(9):1394–1401.

|relevance=The findings of this paper have practical implications for fighting bullying in schools. Instead of drafting top-down policies to reduce bullying on a community level, students should instead be encouraged on an individual level to speak out against conflict. By doing so, students will adjust their behaviors based on perceived social norms.
|journal=Proceedings of the National Academy of Sciences
|pub_date=2015
|doi=10.7910/DVN/29199
|subject=Sociology
}}

Changing climates of conflict: A social network experiment in 56 schools

2018-04-02T02:49:09Z

Ahounsel:

{{Summary
|title=Changing climates of conflict: A social network experiment in 56 schools
|authors=Elizabeth Paluck, Hana Sheperd, Peter Aronow
|tags=Social Influence, Social Norms, Bullying, Adolescents, Social Psychology
|summary=It has been proposed that humans base their behavior on perceived social norms in the communities that they are situated in. Paluck et. al evaluate this theory by developing an experiment on conflict reduction in New Jersey schools. They find that when students encourage conflict-reducing behavior among their online social networks, there is a reduction of disciplinary reports by 30% over 1 year.

==Problem==
Several approaches have been proposed for reducing negative behavior and/or encouraging positive behavior across a community. However, much of the debate has been on a theoretical level rather than a practical level. As such, it is not clear what approaches should be used in real-world situations.

==Design==
To determine how community-wide behavior can be altered in the real world, Paluck designed an experiment for reducing conflict among students in New Jersey schools. Her experiment incorporates three popular theories:
# Developing behavioral interventions that are targeted towards an individual's psychology
# Scaling up behavioral interventions with regulation or community-level persuasion
# Altering behavior through peer influence

==Methods==
28 of 56 New Jersey schools were randomly selected to receive the intervention. Within these schools, a group of students were randomly selected that represented 15% of the school's population, and half of these students were selected to participate in the experiment. These students would go to optional weekly meetings to develop strategies for conflict reduction with the assistance of a trained researcher. The students would then encourage their peers via social media to adopt these behaviors.

==Results==
On average, the intervention reduced the number of disciplinary reports by 30%. Furthermore, students that were identified among their peers as "social referents" had an even greater effect on encouraging conflict-reducing behaviors. When 20% of the students that received the intervention in a given school were identified as social referents, the school experienced a 60% reduction in disciplinary reports. Lastly, when social referents received the intervention, other students were less likely to believe that conflict is a normal thing. These results show that social referents are best equipped to spread conflict-reducing behaviors across a community through peer influence.

==Notable References==

|relevance=The findings of this paper have practical implications for fighting bullying in schools. Instead of drafting top-down policies to reduce bullying on a community level, students should instead be encouraged on an individual level to speak out against conflict. By doing so, students will adjust their behaviors based on perceived social norms.
|journal=Proceedings of the National Academy of Sciences
|pub_date=2015
|doi=10.7910/DVN/29199
|subject=Sociology
}}

Changing climates of conflict: A social network experiment in 56 schools

2018-04-02T02:20:39Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-02T02:09:52Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-02T02:08:01Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-02T02:05:57Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T23:03:09Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T23:02:40Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T23:01:29Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:53:59Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:42:24Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:42:00Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:40:09Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:34:36Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:32:32Z

Ahounsel:

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:20:56Z

Ahounsel: Add structure

{{Summary
|title=Changing climates of conflict: A social network experiment in 56 schools
|authors=Elizabeth Paluck, Hana Sheperd, Peter Aronow
|tags=Social Influence, Social Norms, Bullying, Adolescents, Social Psychology
|journal=Proceedings of the National Academy of Sciences
|pub_date=2015
|doi=10.7910/DVN/29199
|subject=Sociology
}}

==Problem==

==Design==

==Methods==

==Results==

==Notable References==

User:Ahounsel

2018-04-01T22:18:19Z

Ahounsel:

I'm a first-year Ph.D. student in the Computer Science department at Princeton University. My advisor is Nick Feamster. Generally speaking, I'm interested in Internet censorship, free expression, privacy, and tech policy.

Changing climates of conflict: A social network experiment in 56 schools

2018-04-01T22:15:41Z

Ahounsel: Created page with "{{Summary |title=Changing climates of conflict: A social network experiment in 56 schools |authors=Elizabeth Paluck, Hana Sheperd, Peter Aronow |tags=Social Influence, Social..."

{{Summary
|title=Changing climates of conflict: A social network experiment in 56 schools
|authors=Elizabeth Paluck, Hana Sheperd, Peter Aronow
|tags=Social Influence, Social Norms, Bullying, Adolescents, Social Psychology
|journal=Proceedings of the National Academy of Sciences
|pub_date=2015
|doi=10.7910/DVN/29199
|subject=Sociology
}}

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:41:13Z

Ahounsel:

{{Summary
|title=Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments
|authors=David Broockman, Donald Green
|tags=Political Science, Field Experiments, Advertising
|summary=Do online ads for a political candidate positively influence a viewer's opinion of the candidate? Two political scientists conducted large-scale, randomized field experiments to answer this question. They found that exposure to such ads does not make viewers more likely to remember or think positively of the depicted candidate.

==Problem==
The Internet has become a near universal method of mass communication. In response to this, political candidates have increasingly placed political advertisements on popular websites like Facebook. However, it is unclear whether or not these advertisements are actually able to influence public opinion.

==Design==
To answer this question, the researchers designed two large-scale, randomized field experiments on Facebook. In the first experiment, nearly 15,000 users were placed in a treatment group for exposure to ads for a Republican state-legislature candidate. On average, these users saw the ads 36 times. In the second experiment, approximately 25% of 261,150 eligible voters were placed in a treatment group for exposure to ads for a Democratic congressional candidate. On average, these users saw the ads 36.6 times.

==Methods==
In order to evaluate the impact of the political ads, an automated phone survey was conducted on members of the treatment group for each study. A handful of questions were asked in the following order:
# Whether or not the voter had a positive impression of the candidate that collaborated with the study
# Whether or not the voter had a positive impression of the opposing candidate
# Whether the voter recalled the collaborating candidate's main issues, which were discussed on the ad
# Whether the voter recalled seeing any ads for the candidate on Facebook
# The amount of times the voter had used Facebook in the past week

The last two questions were intentionally placed at the end in order to prevent the voters from realizing that they participated in a study on Facebook before the dependent variables could be measured.

==Results==
The results of Study 1 show that the political ads did not significantly affect voters' knowledge of the candidates' platform or what they think about the candidate. The same results were found in Study 2, with the exception that a larger percentage of voters did recall seeing the ads. Thus, the placement of ads that support a political candidate on Facebook do not seem to positively influence public opinion of them.

==Notable References==
Carlisle, J.E. & Patton, R.C. (2013). Is social media changing how we understand political engagement? An analysis of Facebook and the 2008 presidential election. Political Research Quarterly. Forthcoming.

Google (2013). Get out the vote—Google politics and elections ad toolkit. http://www.google.com/ads/elections/get-out-vote.html.
|relevance=There has been much concern regarding the impact of Russian manipulation of the 2016 United States presidential election through the use of political ads on Facebook. Although this paper was written in 2014, it contributes to the public's understanding of how effective political advertisements are on Facebook, and thus the effectiveness of Russian manipulation.
|journal=Political Behavior
|pub_date=2014
|doi=10.1007/s11109-013-9239-z
}}

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:40:25Z

Ahounsel:

{{Summary
|title=Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments
|authors=David Broockman, Donald Green
|tags=Political Science, Field Experiments, Advertising
|summary=Do online ads for a political candidate positively influence a viewer's opinion of the candidate? Two political scientists conducted large-scale, randomized field experiments to answer this question. They found that exposure to such ads does not make viewers more likely to remember or think positively of the depicted candidate.

==Problem==
The Internet has become a near universal method of mass communication. In response to this, political candidates have increasingly placed political advertisements on popular websites like Facebook. However, it is unclear whether or not these advertisements are actually able to influence public opinion.

==Design==
To answer this question, the researchers designed two large-scale, randomized field experiments on Facebook. In the first experiment, nearly 15,000 users were placed in a treatment group for exposure to ads for a Republican state-legislature candidate. On average, these users saw the ads 36 times. In the second experiment, approximately 25% of 261,150 eligible voters were placed in a treatment group for exposure to ads for a Democratic congressional candidate. On average, these users saw the ads 36.6 times.

==Methods==
In order to evaluate the impact of the political ads, an automated phone survey was conducted on members of the treatment group for each study. A handful of questions were asked in the following order:
# Whether or not the voter had a positive impression of the candidate that collaborated with the study
# Whether or not the voter had a positive impression of the opposing candidate
# Whether the voter recalled the collaborating candidate's main issues, which were discussed on the ad
# Whether the voter recalled seeing any ads for the candidate on Facebook
# The amount of times the voter had used Facebook in the past week

The last two questions were intentionally placed at the end in order to prevent the voters from realizing that they participated in a study on Facebook before the dependent variables could be measured.

==Results==
The results of Study 1 show that the political ads did not significantly affect voters' knowledge of the candidates' platform or what they think about the candidate. The same results were found in Study 2, with the exception that a larger percentage of voters did recall seeing the ads. Thus, the placement of ads that support a political candidate on Facebook do not seem to positively influence public opinion of them.

==Notable References==
Carlisle, J.E. & Patton, R.C. (2013). Is social media changing how we understand political engagement? An analysis of Facebook and the 2008 presidential election. Political Research Quarterly. Forthcoming.

Google (2013). Get out the vote—Google politics and elections ad toolkit. http://www.google.com/ads/elections/get-out-vote.html.
|relevance=There has been much concern regarding the impact of Russian manipulation of the 2016 United States presidential election through the use of political ads on Facebook. Although this paper was written in 2014, it contributes to the public's understanding of how effective political advertisements are on Facebook.
|journal=Political Behavior
|pub_date=2014
|doi=10.1007/s11109-013-9239-z
}}

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:37:53Z

Ahounsel:

{{Summary
|title=Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments
|authors=David Broockman, Donald Green
|tags=Political Science, Field Experiments, Advertising
|summary=Do online ads for a political candidate positively influence a viewer's opinion of the candidate? Two political scientists conducted large-scale, randomized field experiments to answer this question. They found that exposure to such ads does not make viewers more likely to remember or think positively of the depicted candidate.

==Problem==
The Internet has become a near universal method of mass communication. In response to this, political candidates have increasingly placed political advertisements on popular websites like Facebook. However, it is unclear whether or not these advertisements are actually able to influence public opinion.

==Design==
To answer this question, the researchers designed two large-scale, randomized field experiments on Facebook. In the first experiment, nearly 15,000 users were placed in a treatment group for exposure to ads for a Republican state-legislature candidate. On average, these users saw the ads 36 times. In the second experiment, approximately 25% of 261,150 eligible voters were placed in a treatment group for exposure to ads for a Democratic congressional candidate. On average, these users saw the ads 36.6 times.

==Methods==
In order to evaluate the impact of the political ads, an automated phone survey was conducted on members of the treatment group for each study. A handful of questions were asked in the following order:
# Whether or not the voter had a positive impression of the candidate that collaborated with the study
# Whether or not the voter had a positive impression of the opposing candidate
# Whether the voter recalled the collaborating candidate's main issues, which were discussed on the ad
# Whether the voter recalled seeing any ads for the candidate on Facebook
# The amount of times the voter had used Facebook in the past week

The last two questions were intentionally placed at the end in order to prevent the voters from realizing that they participated in a study on Facebook before the dependent variables could be measured.

==Results==
The results of Study 1 show that the political ads did not significantly affect voters' knowledge of the candidates' platform or what they think about the candidate. The same results were found in Study 2, with the exception that a larger percentage of voters did recall seeing the ads. Thus, the placement of ads that support a political candidate on Facebook do not seem to positively influence public opinion of them.

==Notable References==
Carlisle, J.E. & Patton, R.C. (2013). Is social media changing how we understand political engagement? An analysis of Facebook and the 2008 presidential election. Political Research Quarterly. Forthcoming.

Google (2013). Get out the vote—Google politics and elections ad toolkit. http://www.google.com/ads/elections/get-out-vote.html.
|journal=Political Behavior
|pub_date=2014
|doi=10.1007/s11109-013-9239-z
}}

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:36:21Z

Ahounsel:

{{Summary
|title=Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments
|authors=David Broockman, Donald Green
|tags=Political Science, Field Experiments, Advertising
|summary=Do online ads for a political candidate positively influence a viewer's opinion of the candidate? Two political scientists conducted large-scale, randomized field experiments to answer this question. They found that exposure to such ads does not make viewers more likely to remember or think positively of the depicted candidate.

==Problem==
The Internet has become a near universal method of mass communication. In response to this, political candidates have increasingly placed political advertisements on popular websites like Facebook. However, it is unclear whether or not these advertisements are actually able to influence public opinion.

==Design==
To answer this question, the researchers designed two large-scale, randomized field experiments on Facebook. In the first experiment, nearly 15,000 users were placed in a treatment group for exposure to ads for a Republican state-legislature candidate. On average, these users saw the ads 36 times. In the second experiment, approximately 25% of 261,150 eligible voters were placed in a treatment group for exposure to ads for a Democratic congressional candidate. On average, these users saw the ads 36.6 times.

==Methods==
In order to evaluate the impact of the political ads, an automated phone survey was conducted on members of the treatment group for each study. A handful of questions were asked in the following order:
# Whether or not the voter had a positive impression of the candidate that collaborated with the study
# Whether or not the voter had a positive impression of the opposing candidate
# Whether the voter recalled the collaborating candidate's main issues, which were discussed on the ad
# Whether the voter recalled seeing any ads for the candidate on Facebook
# The amount of times the voter had used Facebook in the past week

The last two questions were intentionally placed at the end in order to prevent the voters from realizing that they participated in a study on Facebook before the dependent variables could be measured.

==Results==
The results of Study 1 show that the political ads did not significantly affect voters' knowledge of the candidates' platform or what they think about the candidate. The same results were found in Study 2, with the exception that a larger percentage of voters did recall seeing the ads. Thus, the placement of ads that support a political candidate on Facebook do not seem to positively influence public opinion of them.

==Notable References==
|journal=Political Behavior
|pub_date=2014
|doi=10.1007/s11109-013-9239-z
}}

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:32:52Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:28:29Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:25:50Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:20:14Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:13:21Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T02:00:20Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T01:56:34Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T01:48:35Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-04T01:46:54Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-03T22:50:29Z

Ahounsel:

Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments

2018-03-03T22:48:51Z

Ahounsel: Created page with "{{Summary |title=Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments |authors=David Broockma..."

The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

2018-02-26T23:45:47Z

Ahounsel:

{{Summary
|title=The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections
|authors=Robert Epstein, Ronald Robertson
|summary=Can the rankings of search engine results be manipulated to influence voter preferences for election candidates? Robert Epstein and Ronald E. Robertson sought to answer this question through a series of randomized controlled experiments. Their findings show that the preferences of undecided voters can be shifted towards a particular candidate by 20% or more.

==Problem==
Search engines are used by millions of voters during elections to learn about candidates. Previous work has shown that users tend to focus on websites that rank highly in search results, even if these websites are not necessarily related to what they search for. As such, if the rankings of websites that support a particular candidate are manipulated by a search engine to be higher in the results, then voters may receive biased information, potentially influencing election outcomes.

==Design==
Epstein and Robertson conducted a series of randomized controlled experiments to test the effect of search engine manipulation on voter preferences. In the first three experiments, eligible voters from San Diego, CA were recruited to test the effect of such manipulation in a lab setting for a recent election in Australia. The third of these experiments was then replicated as a nation-wide, online experiment across the United States using Amazon's Mechanical Turk platform. Lastly, a large-scale field experiment was conducted in India during a real-world election.

==Methods==
In the first four experiments, voters from various demographics (Republican, Democrat, independent, etc.) read biographies for candidates from a recent election in Australia. After this, they spent 15 minutes searching for more information about these candidates using a mock search engine developed by the researchers. In each treatment group, the rankings of search results were manipulated to favor particular candidates. The final experiment followed a similar design, but it was conducted in India during a real election with real candidates. The demographics for this experiment reflected that of Indian politics.

==Results==
In the first three experiments, there was a 48.4% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 75% of them did not detect that the manipulation was occurring.
In the fourth experiment, there was a 36.7% among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 91.4% of them did not detect that the manipulation was occurring. Lastly, in the field experiment in India, there was a 9.5% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 99.5% of them did not detect that the manipulation was occurring.

==Notable References==
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295.

Fogg, B. J. (2002). Persuasive technology: using computers to change what we think and do. Ubiquity, 2002(December), 5.

|relevance=Put together, these findings suggest that the manipulation of search rankings has a statistically significant effect on voters' likelihood to vote for a particular candidate. Although the magnitude of this effect varied among the five experiments, Epstein and Robertson postulate that it would be fairly easy for a search engine to nudge 20% of undecided voters towards a particular candidate. Thus, without transparency and regulation, search engines could be manipulated to effect the outcome of elections.
|journal=Proceedings of the National Academy of Sciences of the United States of America
|pub_date=2015
|subject=Sociology
}}

The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

2018-02-26T23:45:26Z

Ahounsel: Add references, relevance

{{Summary
|title=The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections
|authors=Robert Epstein, Ronald Robertson
|summary=Can the rankings of search engine results be manipulated to influence voter preferences for election candidates? Robert Epstein and Ronald E. Robertson sought to answer this question through a series of randomized controlled experiments. Their findings show that the preferences of undecided voters can be shifted towards a particular candidate by 20% or more.

==Problem==
Search engines are used by millions of voters during elections to learn about candidates. Previous work has shown that users tend to focus on websites that rank highly in search results, even if these websites are not necessarily related to what they search for. As such, if the rankings of websites that support a particular candidate are manipulated by a search engine to be higher in the results, then voters may receive biased information, potentially influencing election outcomes.

==Design==
Epstein and Robertson conducted a series of randomized controlled experiments to test the effect of search engine manipulation on voter preferences. In the first three experiments, eligible voters from San Diego, CA were recruited to test the effect of such manipulation in a lab setting for a recent election in Australia. The third of these experiments was then replicated as a nation-wide, online experiment across the United States using Amazon's Mechanical Turk platform. Lastly, a large-scale field experiment was conducted in India during a real-world election.

==Methods==
In the first four experiments, voters from various demographics (Republican, Democrat, independent, etc.) read biographies for candidates from a recent election in Australia. After this, they spent 15 minutes searching for more information about these candidates using a mock search engine developed by the researchers. In each treatment group, the rankings of search results were manipulated to favor particular candidates. The final experiment followed a similar design, but it was conducted in India during a real election with real candidates. The demographics for this experiment reflected that of Indian politics.

==Results==
In the first three experiments, there was a 48.4% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 75% of them did not detect that the manipulation was occurring.
In the fourth experiment, there was a 36.7% among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 91.4% of them did not detect that the manipulation was occurring. Lastly, in the field experiment in India, there was a 9.5% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 99.5% of them did not detect that the manipulation was occurring.

Put together, these findings suggest that the manipulation of search rankings has a statistically significant effect on voters' likelihood to vote for a particular candidate. Although the magnitude of this effect varied among the five experiments, Epstein and Robertson postulate that it would be fairly easy for a search engine to nudge 20% of undecided voters towards a particular candidate. Thus, without transparency and regulation, search engines could be manipulated to effect the outcome of elections.

==Notable References==
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295.

Fogg, B. J. (2002). Persuasive technology: using computers to change what we think and do. Ubiquity, 2002(December), 5.

|relevance=Put together, these findings suggest that the manipulation of search rankings has a statistically significant effect on voters' likelihood to vote for a particular candidate. Although the magnitude of this effect varied among the five experiments, Epstein and Robertson postulate that it would be fairly easy for a search engine to nudge 20% of undecided voters towards a particular candidate. Thus, without transparency and regulation, search engines could be manipulated to effect the outcome of elections.
|journal=Proceedings of the National Academy of Sciences of the United States of America
|pub_date=2015
|subject=Sociology
}}

The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

2018-02-26T23:38:20Z

Ahounsel:

{{Summary
|title=The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections
|authors=Robert Epstein, Ronald Robertson
|summary=Can the rankings of search engine results be manipulated to influence voter preferences for election candidates? Robert Epstein and Ronald E. Robertson sought to answer this question through a series of randomized controlled experiments. Their findings show that the preferences of undecided voters can be shifted towards a particular candidate by 20% or more.

==Problem==
Search engines are used by millions of voters during elections to learn about candidates. Previous work has shown that users tend to focus on websites that rank highly in search results, even if these websites are not necessarily related to what they search for. As such, if the rankings of websites that support a particular candidate are manipulated by a search engine to be higher in the results, then voters may receive biased information, potentially influencing election outcomes.

==Design==
Epstein and Robertson conducted a series of randomized controlled experiments to test the effect of search engine manipulation on voter preferences. In the first three experiments, eligible voters from San Diego, CA were recruited to test the effect of such manipulation in a lab setting for a recent election in Australia. The third of these experiments was then replicated as a nation-wide, online experiment across the United States using Amazon's Mechanical Turk platform. Lastly, a large-scale field experiment was conducted in India during a real-world election.

==Methods==
In the first four experiments, voters from various demographics (Republican, Democrat, independent, etc.) read biographies for candidates from a recent election in Australia. After this, they spent 15 minutes searching for more information about these candidates using a mock search engine developed by the researchers. In each treatment group, the rankings of search results were manipulated to favor particular candidates. The final experiment followed a similar design, but it was conducted in India during a real election with real candidates. The demographics for this experiment reflected that of Indian politics.

==Results==
In the first three experiments, there was a 48.4% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 75% of them did not detect that the manipulation was occurring.
In the fourth experiment, there was a 36.7% among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 91.4% of them did not detect that the manipulation was occurring. Lastly, in the field experiment in India, there was a 9.5% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 99.5% of them did not detect that the manipulation was occurring.

Put together, these findings suggest that the manipulation of search rankings has a statistically significant effect on voters' likelihood to vote for a particular candidate. Although the magnitude of this effect varied among the five experiments, Epstein and Robertson postulate that it would be fairly easy for a search engine to nudge 20% of undecided voters towards a particular candidate. Thus, without transparency and regulation, search engines could be manipulated to effect the outcome of elections.

==Notable References==
|journal=Proceedings of the National Academy of Sciences of the United States of America
|pub_date=2015
|subject=Sociology
}}

The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

2018-02-26T23:38:03Z

Ahounsel:

{{Summary
|title=The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections
|authors=Robert Epstein, Ronald Robertson
|summary=Can the rankings of search engine results be manipulated to influence voter preferences for election candidates? Robert Epstein and Ronald E. Robertson sought to answer this question through a series of randomized controlled experiments. Their findings show that the preferences of undecided voters can be shifted towards a particular candidate by 20% or more.

==Problem==
Search engines are used by millions of voters during elections to learn about candidates. Previous work has shown that users tend to focus on websites that rank highly in search results, even if these websites are not necessarily related to what they search for. As such, if the rankings of websites that support a particular candidate are manipulated by a search engine to be higher in the results, then voters may receive biased information, potentially influencing election outcomes.

==Design==
Epstein and Robertson conducted a series of randomized controlled experiments to test the effect of search engine manipulation on voter preferences. In the first three experiments, eligible voters from San Diego, CA were recruited to test the effect of such manipulation in a lab setting for a recent election in Australia. The third of these experiments was then replicated as a nation-wide, online experiment across the United States using Amazon's Mechanical Turk platform. Lastly, a large-scale field experiment was conducted in India during a real-world election.

==Methods==
In the first four experiments, voters from various demographics (Republican, Democrat, independent, etc.) read biographies for candidates from a recent election in Australia. After this, they spent 15 minutes searching for more information about these candidates using a mock search engine developed by the researchers. In each treatment group, the rankings of search results were manipulated to favor particular candidates. The final experiment followed a similar design, but it was conducted in India during a real election with real candidates. The demographics for this experiment reflected that of Indian politics.

==Results==
In the first three experiments, there was a 48.4% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 75% of them did not detect that the manipulation was occurring.
In the fourth experiment, there was a 36.7% among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 91.4% of them did not detect that the manipulation was occurring. Lastly, in the field experiment in India, there was a 9.5% increase among the treatment groups for subjects that would vote for the candidate with biased search rankings. Of these subjects, 99.5% of them did not detect that the manipulation was occurring.

Put together, these findings suggest that the manipulation of search rankings has a statistically significant effect on voters' likelihood to vote for a particular candidate. Although the magnitude of this effect varied among the five experiments, Epstein and Robertson postulate that it would be fairly easy for a search engine to nudge 20% of undecided voters towards a particular candidate. Thus, without transparency and regulation, search engines could be manipulated to effect the outcome of elections.

==Notable References==
|journal=Proceedings of the National Academy of Sciences of the United States of America
|pub_date=2015
|subject=Sociology
}}

The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

2018-02-26T23:33:51Z

Ahounsel: Start adding results

The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections

2018-02-26T20:01:14Z

Ahounsel: