us-präsidentschaftswahl

Wer wählt den US-Präsidenten? Was sind die sogenannten "Swing States"? Und was ist der Unterschied zwischen "primaries" und "caucus"? Testen Sie Ihr. 1. Aug. Welche US-Demokraten schon jetzt als Herausforderer von Donald Trump bei der US-Präsidentschaftswahl in zwei Jahren gehandelt werden. Die Wahl zum Präsidenten und zum Vizepräsidenten der Vereinigten Staaten von Amerika .. („Das Hacken einer US-Präsidentschaftswahl ist noch einfacher als wir dachten!“). Sie fordern eine Reform des US-Wahlsystems und nennen. In Glossary of Casino Terms - C OnlineCasino Deutschland Clip erklärt explainity die einzelnen Schritte der Wahl. Im Artikeltext wurde der präferierte Wert übernommen. Juniabgerufen am We hope to compete in all 50 states. Die Stimmzettel der Präsidentschaftswahl fassen in der Regel diverse Wahlen, Volksabstimmungen und Meinungsbilder zusammen. Januar nach dem Wahltag zur Mittagsstunde zusammentritt, werden pizarro wechsel Stimmen in einer gemeinsamen Sitzung von Repräsentantenhaus und Senat ausgezählt. Sanders will Clinton wählen. Da sollte man nicht den "Messenger killen"! Es ist daher für eine Formel 1 online nicht williams interactive casino free slots, zwei Kandidaten aus einem Staat zu nominieren, da sie sonst bei casino bregenz gutschein einlösen der beiden Wahlen Stimmen verlieren würde. Ist bis zum Bis Oktober rangierte Bush konstant hinter Trump und konnte in einzelnen Bundesstaaten leichte Vorsprünge erzielen. Barry Goldwater Republikanische Partei. Die Anzahl tatsächlich wahlberechtigter Personen ist also sieben bis zehn Prozent geringer, die Anzahl aller registrierten Personen noch geringer.

Der Vorsprung ist zuletzt weiter gesunken. Die Medienanstalten sind sich nicht einig, ob Scott bereits als Sieger erklärt werden kann.

Unabhängig davon, wie diese drei Sitze letztlich verteilt werden, steht die Mehrheit der Republikaner im Senat. Dennoch wäre es für die Republikaner und Donald Trump schon ein Ärgernis, wenn sie weiterhin auf jegliches Abweichen bei Abstimmungen im Senat Rücksicht nehmen müssten.

Ein Vorsprung von 54 zu 46 wäre da schon komfortabler. Ich denke auch, dass es letztlich mindestens 53 Sitze für die Republikaner sein werden.

Im Repräsentantenhaus sind auch keine wesentlichen Änderungen mehr zu erwarten. Die Demokraten haben ihre Mehrheit sicher.

Mit zu Sitzen ist der Vorsprung ordentlich. Aber auch hier gilt es, sich möglichst einige Abweichler unter den Abgeordneten leisten zu können.

Insofern wird auch hier noch auf die 10 verbleibenden Sitze geschaut. Die Ergebnisse stehen aus unterschiedlichen Gründen noch nicht fest.

In einigen Fällen sind die Abstände so knapp, dass es auf wenige Stimmen ankommen wird. In anderen Fällen müssen noch Tausende Briefwahlstimmen gesichtet und ausgewertet werden.

In Kalifornien sind auch noch die Briefwahlstimmen gültig, die am Election Day per Post versandt wurden.

Es ist also nicht ausgeschlossen, dass der letzte verbleibende Sitz erst in der kommenden Woche feststehen wird. Aber darauf wird es nicht mehr ankommen, zumindest nicht in Hinsicht auf die grundlegenden Mehrheitsverhältnisse im Repräsentantenhaus.

Eingestellt von Thomas um Demokraten schaffen den Mehrheitswechsel im Repräsentantenhaus. Republikaner verteidigen ihre Mehrheit im Senat!

Der Liveticker bietet eine Kurzübersicht zum aktuellen Stand der Sitzverteilung, zwei detaillierte Übersichten mit aktuellen Auszählungsständen für die Wahlen zum Senat und Repräsentantenhaus sowie die wichtigsten News aus der Wahlnacht.

Alle Ergebnisse werden ständig aktualisiert. Zur aktualisierten Ansicht, die Seite bitte neu laden. Erläuterung zur Tabelle für den US-Senat: Die Republikaner benötigen 2 dieser 11 Sitze, um die Mehrheit zu halten.

Mehrheit ab 51 Sitze von Sitzen werden 35 neu gewählt. Findings that focused on the positive effects of social media such as incrementing voting turnout Bond, et al.

However, as early as , Philip Howard raised concerns regarding the possibility of manipulating public opinion and spreading political misinformation through social media Howard, These issues have been later proved true by several studies Ratkiewicz, et al.

Of particular concern is the fact social media have been demonstrated effective in influencing individuals Aral and Walker, One way to perform such type of manipulation is by using social bots, algorithmically controlled accounts that emulate the activity of human users but operate at much higher pace e.

Evidence of the adoption of social media bots to attempt manipulating political communication dates back half a decade: The research community reported another similar case around the time of the Massachusetts special election Metaxas and Mustafaraj, Campaigns of this type are sometimes referred to as astroturf or Twitter bombs.

Unfortunately, most of the times, it has proven impossible to determine who's behind these types of operations Kollanyi, et al.

Governments, organizations, and other entities with sufficient resources, can obtain the technological capabilities to deploy thousands of social bots and use them to their advantage, either to support or to attack particular political figures or candidates.

Indeed, it has become increasingly simpler to deploy social bots, so that, in some cases, no coding skills are required to setup accounts that perform simple automated activities: Various source codes for sophisticated social media bots can be found online as well, ready to be customized and optimized by the more technical savvy users Kollanyi, We inspected several of these readily available bots and this is a non-comprehensive list of the capabilities that they provide: Most of these bots can run in cloud services or infrastructures like Amazon Web Services AWS or Heroku, making it more difficult to block them.

Advanced conversational bots powered by more sophisticated Artificial Intelligences are provided by companies like ChatBots.

Much research has been devoted recently to reverse-engineer social bot strategies from observed activities, to understand who they target, how they generate content, when they take action, and what topics they talk about Yang, et al.

Ultimately, this may lead to the identification of their controllers, namely the bot masters. In this paper, we describe the investigation that brought us to unveil the pervasive presence and activity of social bots involved in the U.

Presidential election conversation ongoing on social media. We collected Twitter data for an extensive period prior to the election that includes all three Presidential debates.

By continuously polling the Twitter Search API for relevant, election-related, content using hashtag- and keyword-based queries, we obtained a large dataset of over 20 million tweets generated between 16 September and 21 October by about 2.

Advanced machine learning techniques used to discover social bots developed by our group in the past Yang, et al. We investigated the temporal dynamics of the social media conversation, to study how it reflects shocks from external events e.

We analyzed the geographical dimension as well, by leveraging Twitter metadata available for a subset of tweets, verifying that bots and humans exhibit very different geographical provenance.

We finally investigated what influence social bots have on the structure of the network and on communication dynamics, assessing their degree of embeddedness by means of k -core decomposition analysis.

We manually crafted a list of hashtags and keywords that relate to the U. To make sure our query list is comprehensive, we also added a few search terms for two third party candidates, namely Libertarian Party nominee Gary Johnson one term , and Green Party nominee Jill Stein two terms.

The full list of search terms is reported in Table 1 , along with the total number of tweets containing each keyword term note that a single tweet may contain more than one key term, therefore some overlap exists.

No significant number of tweets is being generated for the third party candidates, therefore in the following we will focus our analysis only on Trump and Clinton.

By querying the Twitter Search API at regular intervals of 10 seconds, continuously and without interruptions in three periods between 16 September and 21 October , we collected a large dataset constituted by Table 2 reports some aggregate statistics of the dataset.

The data collection infrastructure ran inside an Amazon Web Services AWS instance to ensure resilience and scalability.

Determining whether either human or a bot controls a social media account has proven a very challenging task Ferrara, et al. Our prior efforts produced an openly accessible solution called BotOrNot Davis, et al.

BotOrNot is a machine-learning framework that extracts and analyses a set of over one thousand features, spanning content and network structure, temporal activity, user profile data, and sentiment analysis to produce a score that suggests the likelihood that the inspected account is indeed a social bot.

Extensive analysis revealed that the two most important classes of feature to detect bots are, maybe unsurprisingly, the metadata and usage statistics associated with the user accounts.

The following indicators provide the strongest signals to separate bots from humans: We point the reader interested in further technical details to our prior work Ferrara, et al.

BotOrNot has been trained with thousands of instances of social bots, from simple to sophisticated, and an accuracy of above 95 percent Davis, et al.

Typically, BotOrNot yields likelihood scores above 50 percent only for accounts that look suspicious to a scrupulous analysis. The Python BotOrNot API queries the Twitter API to extract the most tweets and all the publicly available account metadata, and feed this features to an ensemble of machine learning classifiers, which produce a bot score.

To label accounts as bots, we use the fifty-percent threshold — which has proven effective in prior studies Davis, et al.

Figure 1 shows the distribution of bot scores yielded by BotOrNot: This suggests that no significant difference in bot classification would occur if we were to increase the threshold used to label accounts as bots.

Interestingly, a mild bimodality is visible, with a clear bump in the bot scores around 0. Therefore, we tested the top 50, accounts ranked by activity volume.

Although these top 50 thousand users account for roughly only two percent of the entire population, it is worth noting that they are responsible for producing over This choice gives us sufficient statistical power to extrapolate the distribution of bots and humans for the entire population without the need to test accounts that are only marginally involved in the conversation.

Out of the top 50 thousand accounts, BotOrNot assigned a bot score greater than the established 0. A total of 40, users responsible for Even if all the 2, users were bots, and Twitter suspended their accounts for violating the terms of service, this would suggest that roughly 70 percent of the total bot population the remainder 7, accounts was still active on the platform at the time of our verification.

By extrapolating for the entire population, we estimate the presence of at least thousand bots, accounting for roughly 15 percent of the total Twitter population active in the U.

Presidential election discussion, and responsible for about 3. These statistics are summarized in Table 3. To understand how bots and humans discuss about the Presidential candidates we will rely upon sentiment analysis.

To attach a sentiment score to the tweets in our dataset, we use SentiStrength Thelwall, et al. SentiStrength is a sentiment analysis algorithm which has been specifically designed to annotate social media data.

This design choice provides some desirable advantages: Applications of SentiStrength to social media data found it particularly effective at capturing positive and negative emotions with, respectively, We tested it extensively and also used it in prior studies to validate the effect of sentiment on the diffusion of information in social media Ferrara and Yang, Starting from the polarity scores, we capture the sentiment of each tweet t with one single measure, the sentiment score S t , defined as the difference between positive and negative sentiment scores: Our analysis is aimed at investigating three directions, discussed separately in the following: In Figure 2 , we visualize the timeline of volume of tweets present in our dataset, during three periods between 16 September and 21 October , during which we collected data from Twitter.

The figure also provides annotation of the four political debates occurred during this period. The first week 16 September to 24 September serves as a baseline to monitor the baseline political discussion occurred prior to the debates weeks.

The baseline period is followed by one-day break 25 September prior to the first debate, in which we maintained our data collection infrastructure.

The second observation period spans 26 September through 10 October , and it captures three debates first Presidential debate of 26 September, Vice Presidential debate of 4 October, and second Presidential debate of 9 October.

Our system infrastructure required additional maintenance, and we chose the period between 10 October and 16 October for this purpose given the absence of off-line events during that week.

We restarted our data collection for the conclusive period between 16 October and 21 October in time to capture the third and last Presidential debate of 19 October.

We decided to conclude our data collection prior to 22 October when Twitter, along with several other online platforms, was targeted by a large-scale distributed denial of service attack and was down for the majority several hours, making the usage of the platform and thus the data collection impossible.

The baseline observation period 16 September to 25 September shows the circadian activity and weekly cycles typical of social media chatter Golder and Macy, , without particular bursts or spikes related to shocks from external events.

Between 5, and 10, tweets are generated hourly, every day, by users annotated as humans, and roughly 1,—2, are instead generated by accounts labeled as bots, constituting about 10 percent of the total tweets.

The second, and longest, observation window 26 September to 10 October exhibits significantly different communication dynamics if compared to the baseline: We observe systematic spikes of activity as a consequence of the first two debates, respectively on 27 September after the first Presidential debate and on 4 and 5 October after the Vice Presidential debate.

Differently from what stated by some other study that analyzed only the second Presidential debate Kollanyi, et al. What is concerning, however, is the volume of tweets that appear to be consistently and continuously produced by the bots.

This extrapolates to a total of roughly 3. There is an intuitive explanation, supported by the data, to the fact that humans contribute more than bots during bursts, or shocks induced by exogenous events: Humans on the other side, get engaged more easily in online political discussion as a consequence of the occurrence of political events in the off-line world, such as Presidential debates or news releases Effing, et al.

We then considered the geographical dimension of the conversation. Sophisticated bots can make credible accounts by faking profile information, and other metadata, including the geographical provenance, using techniques like gps spoofing Ferrara, et al.

In Figure 3 we plotted the U. The two maps tell significantly different stories: This is strongly aligned with prior findings about the geographic distribution of political discussion in the U.

Partisanship and supporting activity: We next inferred the partisanship of the users in our dataset. We used the five Trump-supporting hashtags donaldtrump, trump, neverhillary, trumppence16, trump and the four Clinton-supporting hillaryclinton, imwithher, nevertrump, hillary to attribute partisanships.

In detail, we employed a simple heuristics based on hashtag adoption: If the majority of hashtags support one particular candidate, we assigned the given user to that political faction Clinton- or Trump-supporter.

This is a very strict and conservative partisanship assignment, likely less prone to misclassification that may be yield by automatic machine-learning techniques not based on manual validation, e.

Our procedure yielded a small, high-confidence, annotated dataset constituted by 7, Clinton supporters bots and 6, humans and 17, Trump supporters 1, bots and 15, humans.

us-präsidentschaftswahl -

Jeder wahlberechtigte Staatsbürger darf in nur einem Bundesstaat wählen. Die Stimmzettel dieser Wahl werden versiegelt und dem amtierenden Vizepräsidenten in seiner Funktion als Präsident des Senats übergeben. Navigation Hauptseite Themenportale Zufälliger Artikel. Januar nach dem Wahltag zur Mittagsstunde zusammentritt, werden die Stimmen in einer gemeinsamen Sitzung von Repräsentantenhaus und Senat ausgezählt. Das aktuelle Verfahren kommt im Wesentlichen seit der Verabschiedung des Horatio Seymour Demokratische Partei. William McKinley Republikanische Partei. Truman war von dieser Regelung als zum Zeitpunkt des Inkrafttretens amtierender Präsident ausgenommen. Viele Bundesstaaten binden die Wahlberechtigung an die Angabe der Social Security Number , obwohl diese Nummer eigentlich nicht als Karteischlüssel verwendet werden darf.

us-präsidentschaftswahl -

Oktober mit kurz zuvor veröffentlichten Transkripten von drei lukrativ bezahlten Vorträgen Clintons vor Vertretern der Investmentbank Goldman Sachs in Verbindung. Präsidenten der Vereinigten Staaten von Amerika ermittelt. Schätzungen zufolge lag die Wahlbeteiligung am 8. November , zugegriffen Dezember Versuche zum Beispiel durch Briefe, E-Mails oder Anrufe, teilweise auch durch Gewalt- und Mordandrohungen [] , Wahlmänner der Republikaner dahingehend zu beeinflussen, nicht für Donald Trump zu stimmen. Band 14, , S. Nach dem knappen Gewinn der Vorwahl in Kentucky lag Clinton weniger als Delegiertenstimmen hinter den für die Nominierung benötigten und appellierte an Bernie Sanders, aufzugeben, um sich auf Donald Trump als Gegner konzentrieren zu können. Dies sind ungebundene Delegierte, die für einen Kandidaten ihrer Wahl stimmen können. Welche Bilanz hinterlässt Präsident Obama? Campaigns of this type are sometimes referred to as astroturf or Twitter bombs. Introduction Methodology Data analysis Conclusions. Viele Anhänger der Republikaner sehen dieses als zu abgehoben an und werfen ihm vor, eine Klientelpolitik zu verfolgen, statt sich von den Interessen der Bevölkerung leiten zu lassen. In particular, we showed that bots are pervasively present and active in the online political discussion about the U. Präsidenten sowie Mike Fulltilt poker mit Stimmen zum Unterschiede zwischen Senat und Repräsentantenhaus. United States Elections Project. Vorwahlergebnisse Beste Spielothek in Lüttewitz-Markritz finden Präsidentschaftswahl in den Vereinigten Staaten März englisch, Kinderspiele online gratis unterschiedliche und williams interactive casino free slots den einzelnen US—Bundesstaaten abweichende Datenlage bzw. Fünf Wahlmänner, die Beste Spielothek in Steinach finden hätten wählen sollen, stimmten ebenfalls für andere Personen.

Us-präsidentschaftswahl -

Assange war vor einem europäischen Haftbefehl aus Schweden wegen Vergewaltigungsvorwürfen in die Botschaft Ecuadors in London geflüchtet, dort sitzt er seit mehr als fünf Jahren fest. Jill Stein Ajamu Baraka. Im Artikeltext wurde der präferierte Wert übernommen. Social Bots im US-Wahlkampf. Die Superdelegierten sind auf dem Nominierungsparteitag abstimmungsberechtigt, aber nicht an das Ergebnis einer Vorwahl gebunden.

Wird ein solches Gesetz nach einem Veto des Präsidenten jedoch durch beide Kammern mit einer Zwei-Drittel-Mehrheit bestätigt, kann der Präsident es nicht mehr verhindern.

Unterschiede zwischen Senat und Repräsentantenhaus. Die Legislaturperioden zwischen Senat und Repräsentantenhaus unterscheiden sich.

Das Repräsentantenhaus wird alle 2 Jahre neu gewählt. In den Senat wird man dagegen für 6 Jahre gewählt, wobei aber alle zwei Jahre ein Drittel des Senats neu zur Abstimmung steht.

Kongresswahlen findet also alle 2 Jahre statt, immer in geraden Jahren. Im Repräsentantenhaus sind auch keine wesentlichen Änderungen mehr zu erwarten.

Die Demokraten haben ihre Mehrheit sicher. Mit zu Sitzen ist der Vorsprung ordentlich. Aber auch hier gilt es, sich möglichst einige Abweichler unter den Abgeordneten leisten zu können.

Insofern wird auch hier noch auf die 10 verbleibenden Sitze geschaut. Die Ergebnisse stehen aus unterschiedlichen Gründen noch nicht fest.

In einigen Fällen sind die Abstände so knapp, dass es auf wenige Stimmen ankommen wird. In anderen Fällen müssen noch Tausende Briefwahlstimmen gesichtet und ausgewertet werden.

In Kalifornien sind auch noch die Briefwahlstimmen gültig, die am Election Day per Post versandt wurden. Es ist also nicht ausgeschlossen, dass der letzte verbleibende Sitz erst in der kommenden Woche feststehen wird.

Aber darauf wird es nicht mehr ankommen, zumindest nicht in Hinsicht auf die grundlegenden Mehrheitsverhältnisse im Repräsentantenhaus.

Eingestellt von Thomas um Demokraten schaffen den Mehrheitswechsel im Repräsentantenhaus. Republikaner verteidigen ihre Mehrheit im Senat!

Der Liveticker bietet eine Kurzübersicht zum aktuellen Stand der Sitzverteilung, zwei detaillierte Übersichten mit aktuellen Auszählungsständen für die Wahlen zum Senat und Repräsentantenhaus sowie die wichtigsten News aus der Wahlnacht.

Alle Ergebnisse werden ständig aktualisiert. Zur aktualisierten Ansicht, die Seite bitte neu laden. We restarted our data collection for the conclusive period between 16 October and 21 October in time to capture the third and last Presidential debate of 19 October.

We decided to conclude our data collection prior to 22 October when Twitter, along with several other online platforms, was targeted by a large-scale distributed denial of service attack and was down for the majority several hours, making the usage of the platform and thus the data collection impossible.

The baseline observation period 16 September to 25 September shows the circadian activity and weekly cycles typical of social media chatter Golder and Macy, , without particular bursts or spikes related to shocks from external events.

Between 5, and 10, tweets are generated hourly, every day, by users annotated as humans, and roughly 1,—2, are instead generated by accounts labeled as bots, constituting about 10 percent of the total tweets.

The second, and longest, observation window 26 September to 10 October exhibits significantly different communication dynamics if compared to the baseline: We observe systematic spikes of activity as a consequence of the first two debates, respectively on 27 September after the first Presidential debate and on 4 and 5 October after the Vice Presidential debate.

Differently from what stated by some other study that analyzed only the second Presidential debate Kollanyi, et al. What is concerning, however, is the volume of tweets that appear to be consistently and continuously produced by the bots.

This extrapolates to a total of roughly 3. There is an intuitive explanation, supported by the data, to the fact that humans contribute more than bots during bursts, or shocks induced by exogenous events: Humans on the other side, get engaged more easily in online political discussion as a consequence of the occurrence of political events in the off-line world, such as Presidential debates or news releases Effing, et al.

We then considered the geographical dimension of the conversation. Sophisticated bots can make credible accounts by faking profile information, and other metadata, including the geographical provenance, using techniques like gps spoofing Ferrara, et al.

In Figure 3 we plotted the U. The two maps tell significantly different stories: This is strongly aligned with prior findings about the geographic distribution of political discussion in the U.

Partisanship and supporting activity: We next inferred the partisanship of the users in our dataset. We used the five Trump-supporting hashtags donaldtrump, trump, neverhillary, trumppence16, trump and the four Clinton-supporting hillaryclinton, imwithher, nevertrump, hillary to attribute partisanships.

In detail, we employed a simple heuristics based on hashtag adoption: If the majority of hashtags support one particular candidate, we assigned the given user to that political faction Clinton- or Trump-supporter.

This is a very strict and conservative partisanship assignment, likely less prone to misclassification that may be yield by automatic machine-learning techniques not based on manual validation, e.

Our procedure yielded a small, high-confidence, annotated dataset constituted by 7, Clinton supporters bots and 6, humans and 17, Trump supporters 1, bots and 15, humans.

Figure 4 and Figure 5 show the Complementary Cumulative Distribution Functions CCDFs of the interactions respectively replies and retweets , initiated by bot and human users.

Each plot disaggregates the interactions in three categories: Both figures exhibit broad distributions typical of social media activity.

What interestingly emerges from contrasting the two figures, is that humans are engaging in replies interactions significantly more one order of magnitude difference with other humans than with bots see right panel of Figure 4.

Conversely, bots fail to substantially engage humans and end up interacting via replies with other bots significantly more than with humans.

Given that bots by design are intended to engage in interactions with humans, our observation goes against what we would have intuitively expected — similar paradoxes have been already previously highlighted in our prior work Ferrara, et al.

One intuitive explanation to this phenomenon is that bots that are not sophisticated enough, cannot produce engaging-enough questions to foster meaningful discussions with humans.

Figure 5 , however, demonstrates that rebroadcasting is a much more effective channel of information spreading: In fact, humans and bots retweet each other substantially at the same rate.

This suggests that bots are being very effective at spreading information in the human population, which could have some nefarious consequences in the cases when humans fail at verifying the correctness and accuracy of such information and information sources.

To further understand how social media users both bots and humans are talking about the two Presidential candidates, we explore the sentiment that the tweets convey.

To this purpose, we rely upon sentiment analysis and in particular on SentiStrength as explained earlier in the Methodology section.

Figure 6 shows four panels: Furthermore, the two left panels show the support to Hillary Clinton respectively by bots and humans , whereas the two right panel show the support to Donald Trump respectively by bots and humans.

This generates a stream of support that is at staggering odds with respect to the overall negative tone that characterizes the Presidential election campaigns.

Tables 4 to 7 show various examples of tweets generated by bots, and the candidate they support detected with our method.

This should illustrate the ability of our framework to study the phenomena at hand. A final consideration emerges when contrasting the pro-Clinton and pro-Trump factions: Conversely, pro-Trump supporters humans and bots devote a significant number of tweets to their opponent: This is strikingly different from the Clinton supporters, whose negative tweets address in large majority the candidate herself, rather than her opponent.

Our final analysis explores the degree of embeddedness of the bots in the social network. To do so, we adopt the k -core decomposition technique, which aims at identifying cores subgroups of nodes all with degree larger than a parameter k.

For example, a core is a subset of nodes in the network, all with degree larger than The intuition is that nodes in cores associated with larger k are more deeply embedded in the network, and therefore sit in more central, or influential, position.

Since we are interested in information diffusion in particular, we created a directed network from the retweets that users exchange one another.

If user u retweets user v , we draw a directed link going from u to v. Therefore, users with very large in-degree will correspond to those who get retweeted a lot.

Starting from this network, we extracted the k -cores, for values of k ranging between 10 and Figure 7 right panel shows the number of users as function of the k -core.

Afterwards, for each k -core we calculated the proportion of users that are human, bot, or unknown. The left panel of Figure 7 shows the results of such analysis: The growth of the two labeled classes follows the intuition that, as k becomes larger, accounts are more active in the conversation, and therefore BotOrNot has more information to classify the accounts.

However, what is interesting is that the fraction of bots that are increasingly better connected and more deeply embedded in the social network grows fourfold, from roughly three percent to above 12 percent.

This insight suggests that bots become more and more central in the rebroadcasting network, and a significant fraction of accounts in high k -cores is indeed a social bot.

The diffusion of information and the mechanisms of democratic discussion have radically changed since the advent of online social media. Platforms like Twitter have been extensively praised for their contribution to democratization of discussions about policy, politics, and social issues.

However, many studies have also highlighted the perils associated with the abuse of these platforms.

Manipulation of information, and the spreading of misinformation and unverified information are among those risks.

Seit Ende Juli dominierte Donald Trump in fast sämtlichen nationalen und bundesstaatlichen Umfragen das Bewerberfeld. Horatio Seymour Demokratische Partei. Obama würde Hillary Clinton unterstützen. Clinton verfiel insbesondere in kritischen Zeiten ihrer Karriere in genderspezifisch unterschiedlich verstandene Ausdrucksweisen. Oktober , abgerufen am Zudem sind mehrere Klagen gegen Trump wegen der unzureichenden Trennung von seinen unternehmerischen Interessen anhängig. Die Wahlbeteiligung hat sich in den letzten Jahren verringert, obwohl sie sich während der Wahl etwas von den Wahlen und erholte. Nur nicht so intelligent. Grundsätzlich hat jeder Bundesstaat das Recht zu entscheiden wie er seine Wahlmänner vergibt. Er hatte im Show- und Celebrityumfeld seit Jahrzehnten Erfahrung und entsprechende Vernetzung und wurde bevorzugt zitiert und besprochen.

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Us-präsidentschaftswahl

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