12 research outputs found

    Petition Growth and Success Rates on the UK No. 10 Downing Street Website

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    Now that so much of collective action takes place online, web-generated data can further understanding of the mechanics of Internet-based mobilisation. This trace data offers social science researchers the potential for new forms of analysis, using real-time transactional data based on entire populations, rather than sample-based surveys of what people think they did or might do. This paper uses a `big data' approach to track the growth of over 8,000 petitions to the UK Government on the No. 10 Downing Street website for two years, analysing the rate of growth per day and testing the hypothesis that the distribution of daily change will be leptokurtic (rather than normal) as previous research on agenda setting would suggest. This hypothesis is confirmed, suggesting that Internet-based mobilisation is characterized by tipping points (or punctuated equilibria) and explaining some of the volatility in online collective action. We find also that most successful petitions grow quickly and that the number of signatures a petition receives on its first day is a significant factor in explaining the overall number of signatures a petition receives during its lifetime. These findings have implications for the strategies of those initiating petitions and the design of web sites with the aim of maximising citizen engagement with policy issues.Comment: To appear in proceeding of WebSci'13, May 1-5, 2013, Paris, Franc

    Modeling the Rise in Internet-based Petitions

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    Contemporary collective action, much of which involves social media and other Internet-based platforms, leaves a digital imprint which may be harvested to better understand the dynamics of mobilization. Petition signing is an example of collective action which has gained in popularity with rising use of social media and provides such data for the whole population of petition signatories for a given platform. This paper tracks the growth curves of all 20,000 petitions to the UK government over 18 months, analyzing the rate of growth and outreach mechanism. Previous research has suggested the importance of the first day to the ultimate success of a petition, but has not examined early growth within that day, made possible here through hourly resolution in the data. The analysis shows that the vast majority of petitions do not achieve any measure of success; over 99 percent fail to get the 10,000 signatures required for an official response and only 0.1 percent attain the 100,000 required for a parliamentary debate. We analyze the data through a multiplicative process model framework to explain the heterogeneous growth of signatures at the population level. We define and measure an average outreach factor for petitions and show that it decays very fast (reducing to 0.1% after 10 hours). After 24 hours, a petition's fate is virtually set. The findings seem to challenge conventional analyses of collective action from economics and political science, where the production function has been assumed to follow an S-shaped curve.Comment: Submitted to EPJ Data Scienc

    Please Sign to Save... : How Online EnvironmentalPetitions Succeed

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    Social media have become one of the key platforms to support the debate on climate change. In particular, Twitter allows easy information dissemination when running environmental campaigns. Yet, the dynamics of these campaigns on social platforms still remain largely unexplored. In this paper, we study the success factors enabling online petitions to attain their required number of signatures. We present an analysis of e-petitions and identify how their number of users, tweets and retweets correlate with their success. In addition, we show that environmental petitions are actively promoted by popular public campaigns on Twitter. Finally, we present an annotated corpus of petitions posted by environmental campaigns together with their corresponding tweets to enable further exploration

    Investigating Political Participation and Social Information Using Big Data and a Natural Experiment

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    Social information is particularly prominent in digital settings where the design of platforms can more easily give real-time information about the behaviour of peers and reference groups and thereby stimulate political activity. Changes to these platforms can generate natural experiments allowing an assessment of the impact of changes in social information and design on participation. This paper investigates the impact of the introduction of trending information on the homepage of the UK government petitions platform. Using interrupted time series and a regression discontinuity design, we find that the introduction of the trending feature had no statistically significant effect on the overall number of signatures per day, but that the distribution of signatures across petitions changes: the most popular petitions gain even more signatures at the expense of those with less signatories. We find significant differences between petitions trending at different ranks, even after controlling for each petition's individual growth prior to trending. The findings suggest a non-negligible group of individuals visit the homepage of the site looking for petitions to sign and therefore see the list of trending petitions, and a significant proportion of this group responds to the social information that it provides. These findings contribute to our understanding of how social information, and the form in which it is presented, affects individual political behaviour in digital settings.Comment: Prepared for delivery at the 2014 Annual Meeting of the American Political Science Association, August 28-31, 201

    The impact of open data in the UK: complex, unpredictable, and political

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    This article examines the democratic impact of the UK coalition government's Transparency Agenda, focusing on the publication of all local government spending over Β£500 by councils in England. It looks at whether the new data have driven increased democratic accountability, public participation, and information transmission. The evidence suggests that the local government spending data have driven some accountability. However, rather than forging new β€˜performance regimes’, creating β€˜armchair auditors’, or bringing mass use and involvement, the publication creates a further element of political disruption. Assessment of the use and impact of the new spending data finds it is more complex, more unpredictable, and more political than the rhetoric around Open Data indicates. The danger is that the gap between aims and impact invites disappointment from supporters

    A Multi-Appeal Model of Persuasion for Online Petition Success: A Linguistic Cue-Based Approach

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    Online petitions have become a powerful tool used by the public to affect change in society. Despite the increasing popularity of these petitions, it remains unclear how the public consumes and interprets their content and how this helps the creators of online petitions achieve their goals. This study investigates how linguistic factors present in online petition texts influence petition success. Specifically, drawing upon the dual-process theory of persuasion and the moral persuasion literature, this study examines cognitive, emotional, and moral linguistic factors in petition texts and identifies how they contribute to the success or failure of online petitions. The results, which are based on an analysis of 45,377 petitions from Change.org, show that petitions containing positive emotions and enlightening information are more likely to succeed. Contrary to popular belief, petitions containing heavy cognitive reasoning and those emphasizing moral judgment are less likely to succeed. This study also exemplifies the use of an analytical approach for examining crowd-sourced content involving online political phenomena related to policy-making, governance, political campaigns, and large social causes

    μ˜μ œμ„€μ •μ΄λ‘ μ˜ μ‹œκ°μ—μ„œ

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :ν–‰μ •λŒ€ν•™μ› ν–‰μ •ν•™κ³Ό(행정학전곡),2019. 8. 엄석진.μ „μžμ²­μ› ν”Œλž«νΌμ΄ λ“±μž₯ν•˜κΈ° μ „κΉŒμ§€λŠ” κ°κ΄€μ μœΌλ‘œ λ‹€μ–‘ν•œ μ΄μŠˆλ“€μ— λŒ€ν•˜μ—¬ ꡭ민듀이 μ§€λ‹Œ 생각, 즉 μ—¬λ‘  λ‚΄μ§€λŠ” 곡둠 ν˜Ήμ€ κ³΅μ€‘μ˜μ œμ— λŒ€ν•œ 관찰은 μƒλ‹Ήνžˆ μ œν•œμ μ΄μ—ˆλ‹€κ³  λ³Ό 수 μžˆλ‹€. κ΄€μ°°λœ κ³΅μ€‘μ˜μ œμ˜ κ²½μš°μ—λ„ λŒ€λΆ€λΆ„μ€ μ‹œμœ„ λ“± μ–΄λ– ν•œ μ΄μŠˆμ— λŒ€ν•œ 직간접적인 이해관계가 μžˆλŠ” μ§‘λ‹¨μ˜ 행동을 ν†΅ν•œ 의견 ν‘œμΆœμ΄ 이뀄진 경우 ν˜Ήμ€ μ„€λ¬Έμ‘°μ‚¬λ‘œ ν™•λ³΄λœ μ†Œμˆ˜ ν‘œλ³Έμ˜ 의견 λ“±μœΌλ‘œλ§Œ 관츑될 수 μžˆμ—ˆμœΌλ©°, 또 이듀 쀑 λŒ€λΆ€λΆ„μ€ 이미 μ–΄λ– ν•œ μ΄μŠˆκ°€ λ°œμƒν•œ ν›„ ν•œμ°Έ 뒀에 μ΄λŸ¬ν•œ μ΄μŠˆμ— λŒ€ν•œ κ³΅μ€‘μ˜μ œμ˜ 관츑이 이루어지며, μ΄λŸ¬ν•œ κ΄€μΈ‘μ˜ κ²°κ³Όκ°€ λŒ€μ€‘μ—κ²Œ μ „λ‹¬λ˜κΈ°κΉŒμ§€λŠ” λ‹€μ‹œ μ–΄λŠ μ •λ„μ˜ μ‹œκ°„μ΄ 흐λ₯΄κΈ°μ— μ μ‹œμ„±λ„ 떨어진닀고 λ³Ό 수 μžˆλ‹€. 반면, μ „μžμ²­μ› ν”Œλž«νΌμ€ μ‹€μ‹œκ°„μœΌλ‘œ 청원인이 μ–΄λ– ν•œ μ΄μŠˆμ— λŒ€ν•œ 청원을 등둝할 수 있으며, μ–΄λŠ κ΅­λ―Όμ—κ²Œλ‚˜ κ³΅κ°œλ˜μ–΄ λˆ„κ΅¬λ‚˜ 이에 λŒ€ν•œ λ™μ˜ μž…μž₯을 ν‘œλͺ…ν•  수 μžˆλ‹€. 이λ₯Ό 톡해 ꡭ민의 μ£Όμš” κ΄€μ‹¬μ‚¬λ‚˜ μ–΄λ– ν•œ μ΄μŠˆμ— λŒ€ν•œ μž…μž₯을 'λ™μ˜ 수' ν˜Ήμ€ 'μ„œλͺ… 수'λΌλŠ” μš”μ†Œλ₯Ό 톡해 κ³„λŸ‰μ μœΌλ‘œ νŒŒμ•…ν•  수 있으며, λ™μ˜ μˆ˜κ°€ λ§Žμ€ 청원은 μƒλŒ€μ μœΌλ‘œ ꡭ민의 생각, 즉, κ³΅μ€‘μ˜μ œλ₯Ό λŒ€λ³€ν•˜λŠ” μ²­μ›μœΌλ‘œ λ³Ό 수 μžˆλ‹€. 특히, 이λ₯Ό μ •λΆ€κ°€ 직접 κ΄€λ¦¬ν•˜λ©΄μ„œ νŠΉμ • 수 μ΄μƒμ˜ μ„œλͺ…을 ν™•λ³΄ν•œ 청원은 μ²­μ™€λŒ€μ—μ„œ 직접 λ‹΅λ³€ν•˜λŠ” λ°©μ‹μœΌλ‘œ κ·Έμ € ꡭ민의 κ³΅μ€‘μ˜μ œλ₯Ό μ œμ‹œν•˜λŠ” κ²ƒλΏλ§Œμ΄ μ•„λ‹ˆλΌ 이λ₯Ό μ •λΆ€κ°€ ν•œμΈ΅ 더 μ‚΄νŽ΄λ³Ό 여지가 μžˆλ‹€λŠ” μ μ—μ„œ ν•΄λ‹Ή μ΄μŠˆμ— κ΄€λ ¨ν•œ 정책에 영ν–₯을 λ―ΈμΉ  수 μžˆλ‹€λŠ” μ μ—μ„œ μ •μ±…ν˜•μ„± μΈ‘λ©΄μ—μ„œμ˜ μ˜μ˜κ°€ μžˆλ‹€. κ·Έλ ‡λ‹€λ©΄ μ΄λ ‡κ²Œ νŽΈλ¦¬ν•˜κ²Œ μ˜κ²¬μ„ λͺ¨μ„ 수 μžˆλŠ” ν”Œλž«νΌμ˜ μš΄μ˜μ— μžˆμ–΄μ„œ ꡭ민듀이 μ–΄λŠ μ΄μŠˆμ— λŒ€ν•΄ κ°•ν•œ 곡감과 λ™μ˜λ₯Ό 보인닀면, μ΄λŠ” μ–΄λ–€ μš”μΈμ˜ 영ν–₯을 λ°›λŠ” κ²ƒμΌκΉŒ? κΈ°μ‘΄ μ •μ±…μ˜μ œμ„€μ • κ³Όμ •μ—μ„œμ²˜λŸΌ 이슈 자체의 νŠΉμ„±μ΄λ‚˜ 이λ₯Ό μ „λ‹¬ν•˜λŠ” 방식이 더 큰 영ν–₯을 λ―ΈμΉ  것인가? ν˜Ήμ€ ν•΄λ‹Ή ν”Œλž«νΌμ΄ μš΄μ˜λ˜λŠ” 온라인 ν™˜κ²½μ΄λΌλŠ” μš”μ†Œκ°€ 더 큰 영ν–₯을 λ―ΈμΉ  것인가? λ³Έ μ—°κ΅¬λŠ” μ΄λ ‡κ²Œ λŒ€μ€‘μ˜ μ˜κ²¬μ„ λͺ¨μœΌκ³  κ³΅μ€‘μ˜μ œλ₯Ό ν˜•μ„±ν•˜μ—¬ μ •λΆ€ 정책에 영ν–₯λ ₯을 κ°€ν•  수 μžˆλŠ” μƒˆλ‘œμš΄ ν†΅λ‘œμΈ ꡭ민청원 ν”Œλž«νΌ λ‚΄μ—μ„œ ν˜•μ„±λ˜λŠ” μ‚¬νšŒμ  μ΄μŠˆμ— λŒ€ν•˜μ—¬ μ‚΄νŽ΄λ³΄κ³ μž ν•œλ‹€. 특히, μ—¬λŸ¬ μ‚¬νšŒμ  μ΄μŠˆκ°€ κ³΅μ€‘μ˜μ œκ°€ λ˜κΈ°κΉŒμ§€ 기쑴의 μ •μ±…μ˜μ œμ„€μ • μ΄λ‘ μ—μ„œ 닀뀄진 μš”μΈλ“€κ³Ό μƒˆλ‘œμš΄ 온라인 ν™˜κ²½μ΄λΌλŠ” μš”μΈλ“€ 쀑 μ–΄λ– ν•œ μš”μΈμ— μ–΄λŠ μ •λ„μ˜ 영ν–₯을 λ°›λŠ”μ§€ μ‹€μ¦μ μœΌλ‘œ λΆ„μ„ν•˜κ³ μž ν•˜λŠ” λͺ©μ μ„ 가지고 μˆ˜ν–‰ν•˜μ˜€λ‹€. 이λ₯Ό μœ„ν•΄ μ •μ±…μ˜μ œμ„€μ •μ΄λ‘ κ³Ό μ˜μ œμ„€μ •κΈ°λŠ₯ μ΄λ‘ μ—μ„œ λ…Όμ˜λœ μš”μΈλ“€, 그리고 Margett(2015)의 μ„ ν–‰μ—°κ΅¬μ—μ„œ ν™œμš©λ˜μ—ˆλ˜ μš”μΈλ“€μ„ λ…λ¦½λ³€μˆ˜λ‘œ μ„€μ •ν•˜μ—¬ λΆ„μ„ν•œλ‹€. 뢄석을 μœ„ν•΄ ꡭ민청원 ν”Œλž«νΌμ—μ„œ 각 μ²­μ›λ“€μ˜ 정보듀을 μˆ˜μ§‘ν•˜μ—¬ ν™œμš©ν•˜κ³ μž ν•˜μ˜€λ‹€. μ •μ±…μ˜μ œμ„€μ • 이둠을 톡해 각 청원이 λ‹΄κ³  μžˆλŠ” 이슈 자체의 νŠΉμ„±κ³Ό ν•΄λ‹Ή λ‚΄μš©μƒ ν•΄λ‹Ή 이슈λ₯Ό μ „λ‹¬ν•˜λŠ” 방법에 μ£Όμ•ˆμ μ„ λ‘λŠ” λ³€μˆ˜λ“€μ„ μ„ λ³„ν•˜μ˜€κ³ , μ˜μ œμ„€μ •κΈ°λŠ₯ 이둠을 ν†΅ν•΄μ„œλŠ” 각 μ΄μŠˆκ°€ μ „νŒŒλ˜λŠ” 온라인 맀체의 μš”μΈμ„ μ„ λ³„ν•˜μ˜€μœΌλ©°, μ΄λŠ” ꡭ민청원 ν”Œλž«νΌ μžμ²΄κ°€ 가지고 μžˆλŠ” ν”Œλž«νΌμ˜ νŠΉμ„±κ³Ό ν•¨κ»˜ 온라인 ν™˜κ²½μ΄λΌλŠ” μš”μΈμœΌλ‘œ λΆ„λ₯˜ν•˜μ—¬ λ³Έ λΆ„μ„μ—μ„œ ν™œμš©ν•˜κ³ μž ν•˜μ˜€λ‹€. λ˜ν•œ, λ³€μˆ˜ μ„€μ • κ³Όμ •μ—μ„œ 영ꡭ과 미ꡭ의 μ „μžμ²­μ› ν”Œλž«νΌμ— λŒ€ν•΄μ„œ 연ꡬ 및 뢄석을 μ‹€μ‹œν•œ Margett(2015)의 μ„ ν–‰ 연ꡬλ₯Ό μ°Έμ‘°ν•˜μ˜€μœΌλ©°, 각 μ²­μ›μ˜ μ„œλͺ… νšλ“ 양상을 μ•Œμ•„λ³΄λŠ” 뢄석에도 ν•΄λ‹Ή 연ꡬλ₯Ό μ°Έμ‘°ν•˜μ˜€λ‹€. ν•΄λ‹Ή μ—°κ΅¬λŠ” 2018λ…„ 12μ›” 31일뢀터 μˆ˜μ§‘μ„ μ‹œμž‘ν•˜μ—¬, 2019λ…„ 2μ›” 28κΉŒμ§€ μ’…λ£Œλœ 청원듀을 λŒ€μƒμœΌλ‘œ ν•œλ‹€. μ—¬κΈ°μ„œ 연ꡬ 뢄석 λ‹¨μœ„λŠ” 각 청원이 되며, 연ꡬ 기간은 데이터 μˆ˜μ§‘ κΈ°κ°„κ³Ό λ™μΌν•œ 2018λ…„ 12μ›” 31일뢀터 2019λ…„ 2μ›” 28μΌκΉŒμ§€κ°€ λœλ‹€. 연ꡬ λΆ„μ„μ—λŠ” 이상값(Outlier)의 영ν–₯을 λœλ°›λŠ” λ‘œλ²„μŠ€νŠΈ 방식을 μ μš©ν•œ λ‹€μ€‘νšŒκ·€λͺ¨ν˜•μ„ ν™œμš©ν•˜μ˜€λ‹€. λ³Έ μ—°κ΅¬μ—μ„œμ˜ μ’…μ†λ³€μˆ˜λŠ” 각 청원이 νšλ“ν•œ μ΅œμ’… μ„œλͺ… μˆ˜μ΄λ‹€. λ³Έ μ—°κ΅¬μ˜ 주된 κ²°κ³Όλ₯Ό μ‚΄νŽ΄λ³΄λ©΄ λ‹€μŒκ³Ό κ°™λ‹€. 첫째, 일뢀 청원 λͺ©μ μ€ 각 μ²­μ›μ˜ μ΅œμ’… μ„œλͺ… νšλ“ μˆ˜μ— 영ν–₯을 λ―ΈμΉ  수 μžˆλ‹€. λ‘˜μ§Έ, 청원인이 μžμ‹ μ„ μ†Œκ°œν•  경우 각 μ²­μ›μ˜ μ΅œμ’… μ„œλͺ… νšλ“ μˆ˜μ— μ •(+)의 영ν–₯을 μ€€λ‹€. μ…‹μ§Έ, 첨뢀 URL μ’…λ₯˜μ— 따라 각 μ²­μ›μ˜ μ΅œμ’… μ„œλͺ… νšλ“ μˆ˜κ°€ λ‹€λ₯΄λ‹€. 특히, 이 쀑 μ†Œμ…œλ―Έλ””μ–΄ κ΄€λ ¨ 첨뢀 URL을 ν™œμš©ν•  경우, 첨뢀 URL을 ν™œμš©ν•˜μ§€ μ•Šμ•˜μ„ λ•Œλ³΄λ‹€ 더 λ§Žμ€ μ„œλͺ… νšλ“ 정도λ₯Ό λ³΄μ΄λŠ” 반면, μ–Έλ‘  κ΄€λ ¨ 첨뢀 URL을 ν™œμš©ν•  κ²½μš°μ—λŠ” μœ μ˜λ―Έν•œ 관계λ₯Ό λ°œκ²¬ν•˜μ§€ λͺ»ν•˜μ˜€λ‹€. μ…‹μ§Έ, 각 청원이 λ“±λ‘λœ λ‚  ν•¨κ»˜ λ“±λ‘λœ μ²­μ›λ“€μ˜ μˆ˜λŠ” μ΅œμ’… μ„œλͺ… νšλ“ μˆ˜μ— λΆ€(-)의 영ν–₯을 μ€€λ‹€. λ„·μ§Έ, νŠΈμœ„ν„°μ—μ„œμ˜ ν¬μŠ€νŒ… μˆ˜κ°€ μ΅œμ’… μ„œλͺ… νšλ“ μˆ˜μ— μ •(+)의 영ν–₯을 μ£ΌλŠ” 반면, 온라인 λ‰΄μŠ€ 기사 μˆ˜λŠ” μ΅œμ’… μ„œλͺ… νšλ“ μˆ˜μ— μœ μ˜λ―Έν•œ κ²°κ³Όλ₯Ό λ‚˜νƒ€λ‚΄μ§€ λͺ»ν•˜μ˜€λ‹€. λ‹€μ„―μ§Έ, 각 청원이 첫 λ‚  νšλ“ν•˜λŠ” μ„œλͺ… λΉ„μœ¨μ€ μ΅œμ’… μ„œλͺ… νšλ“ μˆ˜μ— μ •(+)의 영ν–₯을 μ€€λ‹€. μ•žμ„œ μ–ΈκΈ‰ν•œ κ²ƒμ²˜λŸΌ λ³Έ μ—°κ΅¬λŠ” ꡭ민청원 ν”Œλž«νΌμ΄ 가진 온라인 ν™˜κ²½ μš”μΈ 뿐만 μ•„λ‹ˆλΌ, μ •μ±…μ˜μ œμ„€μ • μ΄λ‘ μ—μ„œ 닀룬 μš”μΈλ“€κ³Ό μ˜μ œμ„€μ •κΈ°λŠ₯ μ΄λ‘ μ—μ„œ 닀룬 μš”μΈλ“€ 각각 μ–΄λ– ν•œ 영ν–₯을 μ§€λ‹ˆλŠ”κ°€μ— λŒ€ν•΄ λΆ„μ„ν•˜κ³ μž ν•˜μ˜€λ‹€. μ΄λŠ” κΈ°μ‘΄ Margett(2015)의 연ꡬ가 μ˜¨λΌμΈμƒμ˜ μ „νŒŒ ν™œλ™κ³Ό μ „μžμ²­μ› ν”Œλž«νΌμ˜ λ””μžμΈμ— μ˜ν•œ μš”μΈμ„ μ€‘μ‹¬μœΌλ‘œ μ‚΄νŽ΄λ³Έλ° λ°˜ν•΄, λ³Έ μ—°κ΅¬μ—μ„œλŠ” ν•΄λ‹Ή μš”μΈλ“€κ³Ό λ”λΆˆμ–΄ μ •μ±…μ˜μ œμ„€μ • 및 μ˜μ œμ„€μ •κΈ°λŠ₯ 이둠 μΈ‘λ©΄μ—μ„œμ˜ μš”μΈλ“€μ„ κ³ λ €ν•˜μ˜€λ‹€λŠ” μ μ—μ„œ 기쑴의 연ꡬ와 차별성을 가지며, 이λ₯Ό 톡해 각 μ΄μŠˆκ°€ μ „μžμ²­μ› ν”Œλž«νΌμ„ 톡해 κ³΅μ€‘μ˜μ œμ— λ„λ‹¬ν•˜λŠ”λ° κΈ°μ‘΄ μ •μ±…μ˜μ œμ„€μ • μš”μΈμ— μ˜ν•΄ 영ν–₯을 λ°›λŠ”μ§€ ν˜Ήμ€ 온라인 ν™˜κ²½μ˜ 영ν–₯을 λ°›λŠ”μ§€ μ’€ 더 닀각적인 μ‹œκ°μœΌλ‘œ λΆ„μ„ν•˜μ˜€λ‹€λŠ” 데에 이 μ—°κ΅¬μ˜ μ˜μ˜κ°€ μžˆλ‹€.제 1 μž₯ μ„œ λ‘  제 1 절 μ—°κ΅¬μ˜ λ°°κ²½κ³Ό λͺ©μ  제 2 절 μ—°κ΅¬μ˜ λŒ€μƒκ³Ό λ²”μœ„ 제 2 μž₯ 이둠적 λ…Όμ˜μ™€ 선행연ꡬ κ²€ν†  제 1 절 이둠적 λ…Όμ˜ 1. μ „μžμ²­μ› ν”Œλž«νΌ 2. μ˜μ œμ„€μ •(Agenda-setting) 이둠 3. μ •μ±…μ˜μ œμ„€μ • 이둠에 κ΄€ν•œ λ…Όμ˜ 4. 온라인 ν™˜κ²½μ—μ„œμ˜ 이슈 μ „νŒŒμ— κ΄€ν•œ λ…Όμ˜ 제 2 절 선행연ꡬ κ²€ν†  1. ν•΄μ™Έ μ „μžμ²­μ› ν”Œλž«νΌμ— κ΄€ν•œ 연ꡬ 2. μ „μžμ²­μ› ν”Œλž«νΌκ³Ό μ •μ±…μ˜μ œ ν˜•μ„±μ˜ 상관성에 κ΄€ν•œ 연ꡬ 3. κΈ°μ‘΄ μ—°κ΅¬μ˜ λΉ„νŒμ  κ²€ν†  제 3 μž₯ 연ꡬ 섀계 제 1 절 μ—°κ΅¬μ˜ 뢄석틀 1. 연ꡬ λͺ¨ν˜• 2. 연ꡬ κ°€μ„€ 제 2 절 λ³€μˆ˜μ˜ κ°œλ…κ³Ό μ‘°μž‘μ  μ •μ˜ 1. 쒅속 λ³€μˆ˜ 2. 독립 λ³€μˆ˜ 3. ν†΅μ œ λ³€μˆ˜ 제 3 절 연ꡬ 방법 1. 뢄석 방법 2. ν™œμš© 데이터 제 4 μž₯ μ²­μ™€λŒ€ ꡭ민청원 ν”Œλž«νΌ κ°œκ΄€ 제 1 절 μ²­μ™€λŒ€ ꡭ민청원 ν”Œλž«νΌ κ°œμš” 제 2 절 ꡭ민청원 ν”Œλž«νΌμ˜ μž‘λ™ 방식 제 3 절 ꡭ민청원 ν”Œλž«νΌμ˜ μΈν„°νŽ˜μ΄μŠ€ 제 4 절 각 청원에 ν¬ν•¨λœ 정보 제 5 절 ꡭ민청원 ν”Œλž«νΌμ˜ 의제 μ„€μ • 영ν–₯λ ₯ 제 5 μž₯ 뢄석 κ²°κ³Ό 제 1 절 ꡭ민청원 ν”Œλž«νΌμ˜ μ„œλͺ… νšλ“ νŠΉμ„± 1. 각 청원 별 μ„œλͺ… νšλ“ κ°œμš” 2. 뢄석 λ°μ΄ν„°μ˜ λΉˆλ„λΆ„μ„/κΈ°μˆ λΆ„μ„ κ²°κ³Ό 제 2 절 λ‹€μ€‘νšŒκ·€λΆ„μ„ κ²°κ³Ό 및 κ°€μ„€ 검증 1. λ‹€μ€‘κ³΅μ„ μ„±μ˜ 검증 2. λ‹€μ€‘νšŒκ·€λΆ„μ„ κ²°κ³Ό 및 κ°€μ„€ 검증 제 3 절 결과의 해석 및 ν† λ‘  제 6 μž₯ κ²°λ‘  제 1 절 연ꡬ 결과의 μš”μ•½ 제 2 절 μ—°κ΅¬μ˜ μ‹œμ‚¬μ κ³Ό ν•œκ³„ μ°Έκ³ λ¬Έν—ŒMaste
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